Research Publications

Authors

Azis, Huzain; Nirmala; Syafie, Lukman; Herman; Fattah Farniwati; Hasanuddin,Tasrif, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This investigation constitutes a noteworthy progression in the advancement of more sophisticated and precise botanical image analysis. The primary objective of this inquiry is to confront the difficulties associated with the categorization of Calendula and Coreopsis flowers through the application of diverse segmentation techniques and classification algorithms. In this experiment, we employed the Canny edge detection, thresholding, mean shift, and Otsu methods to process flower images before applying Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, and Decision Tree algorithms for classification. Enhanced comprehension of the integration of distinct segmentation techniques with varied classification algorithms is attained. We scrutinized accuracy, precision, recall, and F1 measure across diverse segmentation scenarios to assess the efficacy of these algorithms. Our principal discoveries consistently affirm that the Decision Tree algorithm attains the utmost accuracy levels in flower classification when coupled with mean shift segmentation, underscoring its noteworthy proficiency in this endeavor. The pivotal role of an optimal amalgamation of segmentation techniques and classification algorithms in augmenting flower recognition is underscored, thereby charting the course for subsequent investigations into the integration of diverse segmentation methods with advanced classification algorithms. This study's outcomes wield a favorable influence on the domain of botany and image analysis at large, offering support to researchers and scientists in achieving a more precise understanding and classification of plant species.

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Authors

Ikhsan, Muhammad Fiqri Fachrezi; Alwi, Erick Irawadi; Hasanuddin, Tasrif Program Study Informatics Engineering, Faculty of Computer Science, Indonesian Muslim University, Makassar, Indonesia

Abstract

The development of the world of computers, the internet and web technology is so rapid that it has penetrated all areas of people's lives. The increasing number of internet service users means that more and more information can be found online. Many individuals are aware of how the information they provide can be used, and organizations are increasingly aware of information security risks that can have negative impacts. Losing important documents can affect business processes, an organization's image, customer trust, and relationships with their business partners. This incident also occurred at PT. Sadikun Niagamas Raya as a subsidiary of PT. Pertamina. The purpose of this research is to test the security of the www.sadikun.com web domain against attacks from outside parties and convert the penetration testing results into an understandable report. The method used in this research is the Penetration Testing method with several steps starting from Star, searching for information, scanning, testing possible security gaps, creating a test report until completion. The results obtained from this research are that 3 security gaps were found, including scripts that can be inserted and executed in the search column, usernames and passwords that can be accessed in the database due to the ID parameter in the URL being vulnerable to sqlinjection attacks, the database can be downloaded via the URL caused by a configuration error on the server side. Based on the OWASP framework which lists the 10 most common web application security vulnerabilities that have the potential to harm PT. Sadikun Niagamas Raya.

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Authors

Satra,Ramdan, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia; Hadi, Mokh. Sholihul; Sujito, Department of Electrical and Informatics Engineering, Universitas Negeri Malang, Indonesia; Febryan,Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia; Fattah, Muhammad Hattah, Faculty of Fisheries and Marine Sciences, Universitas Muslim Indonesia, Indonesia; Busaeri, Siti Rahbiah, Faculty of Agriculture, Universitas Muslim Indonesia, Indonesia

Abstract

Cultivation of tiger prawns stands as a crucial sector in Indonesia's fisheries industry, significantly contributing to the country's foreign exchange. However, challenges persist in the cultivation process, particularly concerning suboptimal harvest outcomes. A critical factor in tiger prawn cultivation is the water temperature within shrimp ponds, a parameter directly influencing shrimp growth. The recommended normal temperature range is 28-31°C. Deviations from this range can adversely impact the shrimp's metabolic system and appetite, resulting in stress and potential mortality. Temperature fluctuations can lead to severe issues such as hindered growth, reduced productivity, and increased shrimp mortality. Real-time monitoring of air temperature emerges as a pivotal element in ensuring the success of shrimp farming. This research aims to provide a practical solution for shrimp cultivation by presenting a system that enables farmers to adjust air temperature in ponds in real-time through a user-friendly website application. The ability to promptly respond to abnormal temperature fluctuations empowers farmers to optimize cultivation conditions, thereby reducing shrimp mortality rates. The research focuses on creating a water temperature monitoring system for tiger prawn ponds using cloud storage through the Firebase platform. By implementing real-time temperature monitoring, financial risks for shrimp farmers can be mitigated, preventing losses attributed to temperature-induced shrimp mortality. The research utilizes the DS18B20 temperature sensor and WeMos D1 R2 as the control center. The website displays air temperature measurements, showcasing a high accuracy of 99% with a minimal error of 1.2%. This underscores the system's effectiveness in measuring air temperature both above and below the pond. The incorporation of IoT technology for monitoring water quality in ponds offers a practical and innovative approach to tiger prawn cultivation, with the potential to enhance production outcomes in each harvest.

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Authors

Indra, Dolly; Umar, Fitriyani; Fattah, Farniwati; Azis, Huzain; Manga, Abdul Rachman, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The global spread of COVID-19 had altered human behavior. One example was the shift from direct touch to less contact in interpersonal interactions. At that time, during the COVID-19 pandemic, digital technology was vital for reducing and eliminating social, physical, and psychological risk factors and managing the long-term consequences of social isolation and lockdown loneliness. Throughout the previous decade, various nations, notably developing nations, have embraced technology and adapted it to local conditions in response to the pandemic. The technologies are advantageous and might be expanded for further applications. This chapter will discuss deploying various technologies, including an automatic barrier gate, a smart stick for the blind, and automatic handwashing. These instruments utilized microcontroller technology. These tools are helpful, but they require further improvement.

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Authors

Abdullah, Syahrul Mubarak, Department of Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia Indonesia; Pakka, Hariani Ma'tang, Syarifuddin, Andi, Department of Electrical Engineering, Faculty of Engineering, Universitas Muslim Indonesia Indonesia; Alghamdi, Ahmed Saeed,Computer Engineering Department, Taif University, Al Hawiyah Saudi Arabia

Abstract

Android smartphone is currently one of the most extensively utilized operating systems. Nevertheless, Android devices are susceptible to issues such as Ic Emmc, Ic Power, software malfunctions, Blank Screen, Hang, complete device malfunction, and boot loop. Prompt intervention is crucial when a smartphone experiences a problem to prevent more harm and safeguard the user. The Certainty Factor (CF) accounts for the inherent uncertainty in an expert's analysis. Expressions such as "uncertain," "highly probable," "likely," "very likely," "almost certain," and "certain" are frequently employed in this context. This study employed a manual questionnaire to assess the efficacy of the expert system in identifying malfunctions in Android devices. All five technicians and all five user respondents expressed significant agreement about the reliability of the expert system in the questionnaire, and the black box test yielded a perfect 100% success rate. Through accuracy testing, using 10 samples of expert analysis data and 10 samples of system data, it was determined that the expert system achieved an 80% accuracy rate in generating diagnostic conclusions based on the tested data.

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Authors

Purnawansyah; Ali, Zahrizhal; Darwis, Herdianti; Ilmawan, Lutfi Budi; Jabir, Sitti Rahmah; Manga, Abdul Rachman, Department of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Code Large Language Models (Code LLMs) such as Code LLaMa and StarCoder have exhibited outstanding proficiency in tasks required for specific tasks like code generation. Several conducted research to similar task by utilizing fine-tuning techniques from state-of-the-art base models for more specific related task. However, due to the cost limitations and limited computing resources, performing fine-tuning from large language models is excessively high. In this study, we utilized Low-Rank Adaptation (LoRA) for base large language models such as LLaMA-2 and Phi-1.5, which uses trainable rank decomposition matrices. Furthermore, we injected Quantized LoRA (QLoRA) to help reduce memory usage while training the model and analyzed the contribution to GPU usage. Notably, our findings reveal that employing these techniques for fine-tuning on small datasets yields cost-effective and viable alternatives for language-related tasks, showcasing competitive performance compared to state-of-the-art models like CodeLLaMa 7B substantiated by lower train loss achieved in our experiments.

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Abstract

The scope of this book focuses on how information technology may assist in achieving goals and in providing solutions to problems such as a pandemic. Research on the Internet and on technology has been done, and the findings have applications in various sectors that rely on interdisciplinary knowledge. This book explores and describes state-of-the-art research conducted during the COVID-19 pandemic. Topics covered include the IT viewpoint and the rules governing digital transformation throughout the pandemic. The Digital Revolution sped up by a decade during COVID-19, which impacted both the user experience and that of software developers. As a component of the digital transformation process, this book explores the experiences of both the user and developer when attempting to change and adapt while utilizing an information technology program. This book includes five topics: (1) multidisciplinary artificial intelligence, (2) Smart City and Internet of Things applications, (3) game technology and multimedia applications, (4) data science and business intelligence, and (5) IT hospitality and information systems. Each topic is covered in several book chapters with some application in several countries, especially developing countries. The chapters provide insight from contributors with different perspectives and several diverse fields who present new ideas and approaches to solving problems associated with the worldwide pandemic.

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Authors

Purnawansyah, Department of Information Systems, Universitas Muslim Indonesia, Indonesia; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia; Haviluddin, Department of Informatics, Universitas Mulawarman, Indonesia; Raja, Roesman Ridwan; Darwis, Herdianti, Department of Informatics, Universitas Muslim Indonesia, Indonesia; Nafalski, Andrew, UniSA Education Futures, School of Engineering, University of South Australia, Australia

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.

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Authors

Jabir, Sitti Rahmah; Purnawansyah; Darwis Herdianti; Lahuddin Harlinda; Faradibah, Amaliah; Gaffar, Andi Widya Mufila, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Nowadays, Indonesia's tourism sector faced challenges in light of the global recession threat. These challenges encompassed high airline ticket prices and inflation, which in turn influenced consumer spending patterns. To tackle these difficulties, the Ministry of Tourism had taken steps to allow foreign investments in the potential tourism object to invest. The involvement of foreign investors had contributed to substantial growth and advancement within Indonesia's tourism industry, thereby presenting numerous opportunities for prospective investors. Indonesia has set a target of attracting more than 7 million foreign tourists by the year 2023, which has increased double from previous year. Based on the literature, the researcher's objective is to analyze the potential of public tourism sites, categorizing them as viable prospects for potential investors. The data had been obtained from Kaggle which the target variable was the rating from 1 to 5. The initial classification attempt, which utilized these five categories, proved unsatisfactory, prompting the application of unsupervised learning techniques to reduce the number of target variable categories. Through the utilization of k-means clustering, the final classification resulted in two overarching categories: “good” and “bad” ratings. Subsequent analysis revealed that Naïve Bayes emerged as the most effective algorithm for this classification task, albeit with no significant difference in results when compared to support vector machines. In conclusion, future research endeavors might consider exploring alternative unsupervised learning methods or conducting more comprehensive feature selection processes before implementing the classification.

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Authors

Azis, Huzain; Bustam, Faida Daeng, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This dataset contains images of handwritten Lontara numerals ranging from 0 to 9. It comprises a total of 10890 samples, with 1089 images for each numeral class. The images were collected from various individuals to ensure diversity in handwriting styles. Key Features: Classes: 10 (Lontara numerals 0-9) Total Samples: 10890 Samples per Class: 1089 Image Format: Grayscale Data Collection and Labeling: The dataset was created by collecting handwritten numerals from participants with different handwriting styles. Each image was manually labeled to ensure accurate and consistent annotations. The data collection and labeling process was meticulously carried out by one of the authors. Usage: This dataset is suitable for training and testing machine learning models for handwritten numeral recognition. It can be used in various applications such as optical character recognition (OCR) systems, pattern recognition, and other related fields. Contributors: Author 1: Conducted the data collection and labeling process, ensuring accurate and consistent annotations for all samples. Author 2: Handled the data preprocessing, including image normalization and augmentation. Author 3: Developed the script for data collection and managed the overall project coordination. Author 4: Performed the quality check and validation of the dataset. Acknowledgments: We would like to thank all the participants who contributed their handwritten numerals for this dataset. License: CC BY NC 3.0 You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

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Authors

Azis, Huzain; Saly, Intan Novita, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This dataset contains images of handwritten Arabic numerals ranging from 0 to 9. It comprises a total of 9350 samples, with 935 images for each numeral class. The images were collected from various individuals to ensure diversity in handwriting styles. Key Features: Classes: 10 (Arabic numerals 0-9) Total Samples: 9350 Samples per Class: 935 Image Format: Grayscale Image Size: 28x28 pixels (adjust if different) Data Collection and Labeling: The dataset was created by collecting handwritten numerals from participants with different handwriting styles. Each image was manually labeled to ensure accurate and consistent annotations. The data collection and labeling process was meticulously carried out by one of the authors. Usage: This dataset is suitable for training and testing machine learning models for handwritten digit recognition. It can be used in various applications such as optical character recognition (OCR) systems, pattern recognition, and other related fields. Contributors: Author 1: Conducted the data collection and labeling process, ensuring accurate and consistent annotations for all samples. Author 2: Handled the data labelling process. Acknowledgments: We would like to thank all the participants who contributed their handwritten numerals for this dataset. License: CC BY NC 3.0 You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

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Authors

Ilmawan, Lutfi Budi, Department of Informatics Engineering, Universitas Muslim Indonesia, Makassar, Indonesia; Muladi; Prasetya, Didik Dwi, Department of Electrical and Informatics Engineering, Universitas Negeri Malang, Malang, Indonesia

Abstract

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.

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Authors

Seniwati, Rahmatia, Khairul Amri, Ihwana As’ad, Munif Arif Ranti

Abstract

Samalona Island is a tourist destination in Makassar City, South Sulawesi Province, Indonesia. This study aims to identify the potential and challenges in developing digital innovation-based tourism on Samalona Island. The potential of Samalona Island includes a sense of security and the beauty of its underwater nature. This island is a favorite place for foreign and domestic tourists because tourists feel safe when visiting the island. The involvement of all actors, such as stakeholders, industry, media, academics, and local communities, supports the development of Samalona Island. The novelty in this study is the lack of articles discussing Samalona Island based on the pentahelix model. This study found that Samalona Island's development applies the ecotourism principle that focuses on the environment. The role of the media in marketing the potential of Samalona Island attracts domestic and foreign tourists to the island.

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Authors

Qurratu’aina, Khayyirah Annisa; Salim, Yulita; Gaffar, Andi Widya Mufila, Fakultas Ilmu Komputer (Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia)

Abstract

Di era digitalisasi, isu terkait keamanan data dan informasi menjadi salah satu isu yang penting. Menurut data yang dihimpun oleh Badan Siber dan Sandi Negara (BSSN), menjelaskan bahwa dari bulan Januari sampai bulan Agustus 2020, menghasilkan sebanyak 190 juta upaya serangan terhadap web server yang ada di Indonesia. Karena maraknya kasus penyerangan, dibutuhkan upaya untuk mengetahui celah keamanan pada sebuah website untuk meminimalisir resiko penyerangan. Salah satunya pada website Digital School Database milik SMAS Semen Tonasa, yang menjadi objek penelitian ini. Pada penelitian ini, website SMAS Semen Tonasa diuji dengan menggunakan metode Penetration Test untuk menganalisis keamanan pada website. Khususnya menggunakan SQL Injection dan Cross Side Scripting (XSS) sebagai celah keamanan yang ditemukan melalui proses scanning website. Adapun hasil dari penelitian ini menunjukkan bahwa website discas.smasementonasa.sch.id rentan terhadap serangan Cross Side Scripting (XSS) dan SQL Injection yang dibuktikan dengan proses penetration testing yang dilakukan dengan menggunakan tools nikto, acunetix, dan sqlmap. Dari proses pengujian Cross Site Scripting (XSS), celah keamanan XSS website dapat diserang dengan menggunakan script yang dimasukkan ke database, sehingga dapat menyebabkan berubahnya tampilan website. Begitupun dengan pengujian celah keamanan SQL website menggunakan sqlmap, dimana database hingga tabel database website dapat ditemukan yang menyebabkan penyerang dapat melakukan pencurian ataupun pengrusakan database.

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Authors

Dwiyanti, Sri Ulfa; Salim, Yulita; Umar, Fitriyani Universitas Muslim Indonesia

Abstract

Salah satu daerah yang mengeluarkan program untuk meningkatkan kesejahteraan masyarakatnya adalah Provinsi Sulawesi Tenggera tepatnya di Desa Baliara Selatan, Kecamatan Kabaena Barat Kabupaten Bombana. Dari segi tingkat kemiskinan, Kabupaten Bombana masih tergolong tinggi. Artinya, jumlah penduduk dalam keadaan miskin pada tahun 2018 sebanyak 19,77 (seribu jiwa) dan proporsi penduduk miskin sebanyak 4.444 jiwa atau mewakili 11,05%. Hal ini menyebabkan pemerintah Kabupaten Bombana Mengeluarkan kebijakan yaitu menciptakan program-program untuk meningkatkan taraf hidup masyarakat Bombana.Salah satunya program yang diterapkan di Desa Baliara Selatan adalah Program Keluarga Harapan (PKH).Berdasarkan pengalaman yang terjadi, di mana para pendamping staff yang mengalami kesulitan untuk menentukan prioritas penerima bantuan PKH sehingga mengakibatkan proses pengambilan keputusan dan validasi data calon penerima berjalan lambat dan kurang tepat membuat hasil keputusan tidak ideal. Salah satu cara untuk membantu pengambilan keputusan dalam penentuan penerima bantuan adalah menggunakan Sistem Pendukung Keputusan (SPK) dengan metode Multi Attribute Utility Theory (MAUT).Hasil akhirnya adalah serangkaian penilaian alternatif yang menggambarkan keputusan yang dibuat oleh para pengambil keputusan dikantor pusat bombanaHasil keputusan dengan sistem perengkingan terbaik, rangking terbaik di dapat dari hasil perhitungan MAUT. Semakin besar nilai indeks maka semakin bagus pemeringkatan keputusan setiap alternatif.

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Authors

Asis, Muhammad Arfah; Purnawansyah; Salim, Yulita, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Akreditasi memerlukan pengelolaan dokumen yang efisien, namun di banyak universitas, pengelolaan dokumen akreditasi masih menghadapi kendala. Dokumen tersebar dalam berbagai format dan sulit diakses. Oleh karena itu, pengembangan sistem informasi manajemen data akreditasi menjadi penting. Tujuan penelitian ini untuk merancang dan membangun sistem manajemen data akreditasi berbasis web yang sesuai dengan kebutuhan Fakultas Ilmu Komputer di Universitas Muslim Indonesia (UMI). Penelitian ini menggunakan metode waterfall dalam pengembangan sistem dengan tahapan requirements, design, implementation, testing, dan maintenance. Sistem ini memungkinkan admin dan operator mengelola data akreditasi, dan asesor untuk mengakses dan mengevaluasi dokumen akreditasi. Hasil pengujian menunjukkan bahwa sistem dapat berjalan sesuai dengan yang diharapkan, dan semua fitur utama berfungsi dengan baik. Kesimpulannya, sistem ini membantu Fakultas Ilmu Komputer UMI dalam meningkatkan efisiensi dalam proses akreditasi, menghemat waktu dan sumber daya, serta mendukung pemeliharaan kualitas dan reputasi pendidikan tinggi di fakultas.

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