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All Journal International Journal of Public Health Science (IJPHS) Journal of ICT Research and Applications ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Minfo Polgan (JMP) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Hexagon Inspiration: Jurnal Teknologi Informasi dan Komunikasi JEAT : Journal of Electrical and Automation Technology
Hamdan Gani
Politeknik ATI Makassar
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences

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Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022 Gani, Hamdan; Damayanti, Annisa Dwi; Nurani, Nurani; Zuhriyah, Sitti; Jabir, St. Nurhayati; Gani, Helmy; Zhipeng, Feng; Rejeki, Aisyah Sri
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1700.27-37

Abstract

The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. several prolific authors are discovered. However, the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.
Extracting Knowledge for the Success Factors of Digital Games from the Customer Review of E-Commerce Gani, Hamdan
JEAT : Journal of Electrical Automation Technology Vol. 2 No. 1 (2023): JEAT : Journal of Electrical and Automation Technology
Publisher : UPPM Poltek ATI Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61844/jeat.v2i1.473

Abstract

Permainan digital telah menjadi salah satu kegiatan paling populer pada user berbagai usia. Meskipun game digital telah diperkenalkan lebih dari 40 tahun yang lalu, tetapi masih sangat sedikit yang diketahui faktor-faktor apa saja yang menyebabkan sebuah game menjadi sukses di pasaran. Dalam hal ini, penelitian ini menyajikan metode baru dan implementasi praktis untuk menggali pengetahuan tentang faktor-faktor yang membuat game digital sukses di pasaran. Pengetahuan yang diperoleh dari penelitian ini dapat digunakan sebagai rekomendasi bagi perusahaan game dan desainer game dalam strategi pemasaran mereka. Pertama, penelitian ini mengembangkan metode untuk menemukan faktor-faktor yang menyebabkan sebuah game sukses dari review pelanggan e-commerce. Kedua, berdasarkan analisis temuan, penelitian ini menyajikan pengetahuan sebagai rekomendasi strategi pemasaran produk game. Hasil review user terhadap e-commerce digunakan sebagai sumber data untuk dianalisis dalam studi eksperimental. Akhirnya, hasil penelitian ini mengungkapkan bahwa metode yang diusulkan dapat memperoleh rekomendasi pengetahuan.
A Bibliometric Analysis of Augmented Reality and Virtual Reality During 1993–2022 Gani, Hamdan; Ibrar Aprianto, Muhammad
JEAT : Journal of Electrical Automation Technology Vol. 2 No. 2 (2023): JEAT : Journal of Electrical and Automation Technology
Publisher : UPPM Poltek ATI Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61844/jeat.v2i2.680

Abstract

Augmented reality (AR) and virtual reality (VR) have received much attention recently as innovative and valuable technology. With the growth of AR and VR research, a comprehensive examination is required. From the standpoint of bibliometrics, this study conducts a comprehensive analysis of AR and VR papers from 1993 to 2022. A total of 6,785 publications are obtained from the Web of Science (WoS) database and loaded into the professional science mapping tools VOSviewer and Cite Space through preprocessing. The publishing structures are examined using annual publications and the publications of the most productive countries/regions, institutions, and authors. Afterward, the co-citation networks of countries/regions, institutions, authors, and articles are visualized using VOSviewer. Their citation structure and the most influential examples are investigated further. Finally, VOSviewer depicts the collaboration networks of countries/regions, institutions, and writers. Cite Space utilizes timeline analysis and keyword citation burst detection to identify hotspots and research trends. Finally, this study explains a basic understanding of AR and VR for scholars and a detailed examination of AR and VR for future research in this area.
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2290.305-317

Abstract

Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.
PENERAPAN CHATGPT DAN DRAW.IO UNTUK OTOMATISASI FLOWCHART MENGGUNAKAN MERMAID CODE Sidehabi, Sitti Wetenriajeng; Gani, Hamdan; Lutfi
Hexagon Vol 6 No 1 (2025): HEXAGON - Edisi 11
Publisher : Fakultas Teknologi Lingkungan dan Mineral - Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/hexagon.v6i1.5153

Abstract

This research examines the potential of automating flowchart creation by combining ChatGPT, an advanced language model, and Draw.io, an intuitive diagramming tool. The process begins with inputting a workflow description into ChatGPT, which generates Mermaid code to be converted into a visual flowchart in Draw.io. This approach was tested in discrete manufacturing systems courses, where students often struggle to design flowcharts for complex processes. The study is categorized as development research and involves needs analysis, system design, implementation, testing, and evaluation. Results indicate that this method significantly reduces the time and effort needed to create flowcharts, particularly for students without an IT background. Although manual adjustments are still required to meet certain standards, the offered automation provides a solid foundation for further development. The integration of ChatGPT and Draw.io has the potential to enhance understanding of complex systems across various fields, allowing students to focus on analysis and problem-solving rather than time-consuming diagram creation.
Deteksi Pengguna Masker Berbasis Pengolahan Citra Menggunakan Algoritma Yolo Sukriadi, Sukriadi; Gani, Hamdan; Yuyun, Yuyun
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 8 No 1 (2025): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v8i1.274

Abstract

Penelitian ini menerapkan algoritma YOLO untuk mendeteksi pengguna masker serta untuk mengetahui akurasi yang dihasilkan menggunakan algoritma YOLO. Teknologi yang digunakan berbasis Pengolahan Citra. Keluaran dari sistem ini adalah peringatan bagi orang yang tidak menggunakan masker dan menghitung total jumlah pengguna masker dan jumlah yang tidak menggunakan masker. Penelitian ini menggunakan algoritma You Only Look Once (YOLO) generasi ketiga, yang terdiri dari convolutional neural network layer untuk proses ekstraksi fitur dari input serta proses localization objek, dan fully connected layer untuk mengklasifikasikan jenis larva udang. Hasil penelitian ini menunjukkan bahwa sistem pendeteksi pengguna masker tidak mendeteksi dengan baik, hal ini dipengaruhi karena kurangnya cahaya saat pengambilan data uji. Minimal cahaya yang digunakan dalam pengambilan data adalah 400 Lumen. Lumen merupakan satuan pengukuran standar untuk jumlah cahaya yang dapat dihasilkan oleh sebuah sumber cahaya. Dengan menggunakan algoritma YOLO untuk mendeteksi dan menghitung jumlah pengguna masker menghasilkan perhitungan dan deteksi penggunaan masker dengan Dataset yang digunakan pada penelitian ini sebanyak 700 data gambar sebagai data latih dan 70 data uji serta Penerapan algoritma yolo untuk mendeteksi penggunaan masker mencapai tingkat akurasi 97,51%
EVALUATION OF INDOBERT AND ROBERTA: PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT CLASSIFICATION Nur, M. Adnan; Umar, Najirah; Feng, Zhipeng; Gani, Hamdan
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9988

Abstract

The development of Natural Language Processing (NLP) technology has had a significant impact on various fields, especially in sentiment analysis. This analysis becomes important in understanding public perception, especially on social media which has a lot of opinions. Indonesian, with its morphological complexity, dialectal variations, and dynamic everyday vocabulary usage, presents unique challenges in the development of NLP models. This study aims to evaluate and compare the performance of two Indonesian language transformer models, namely IndoBERT (Indonesia Bidirectional Encoder Representations from Transformers) and RoBERTa Indonesia (Robustly Optimized BERT Pretraining Approach) in applying sentiment classification using the Indonesian General Sentiment Analysis Dataset. Both models were fine-tuned using consistent hyperparameter configurations to ensure the validity of the comparison. Evaluation was conducted based on classification metrics, namely precision, recall, F1-score, and accuracy. The results show that the IndoBERT model excels in all aspects of evaluation. IndoBERT achieved an accuracy of 70%, while RoBERTa Indonesia only reached 67%. Additionally, the average F1-score of IndoBERT at 0.69 is higher compared to RoBERTa, which only reached 0.65. The performance of IndoBERT is also more balanced in classifying the three sentiment categories (negative, neutral, and positive), whereas RoBERTa shows less consistent performance, especially in negative and positive sentiments. In the loss analysis, IndoBERT produced a lower evaluation loss value, indicating better generalization capability. Additionally, IndoBERT also shows faster and more stable training times compared to RoBERTa. This performance difference shows that the architecture and pre-trained data used by each model affect their ability to understand Indonesian contextually. This research provides a comprehensive comparative overview of the effectiveness of two transformer models in the task of Indonesian language sentiment analysis, as well as lays the groundwork for selecting a more optimal model in the development of NLP systems for social media.
ESP32-Based Sumo Robot Control System Using PlayStation 4 Controller with Semi-Autonomous Ultrasonic Features Sidehabi, Sitti Wetenriajeng; Mubarak, Muhammad Muflih; Gani, Hamdan
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2591.292-301

Abstract

This study presents the design and implementation of a sumo robot control system integrating an ESP32 Devkit V1 microcontroller with a wireless PlayStation 4 controller and semi-autonomous features based on the HC-SR04 ultrasonic sensor and MG-995 servo motor. The system addresses challenges in sumo robots, including communication stability and control precision. Hardware integration involved DC motors, an L298N driver, and a LiPo battery, while software development used the Arduino IDE with Bluetooth connectivity. Experimental testing demonstrated stable communication with a maximum range of 36 meters, an average controller connection time of 1.998 seconds, and 100% detection accuracy within a 10 cm radius. Push performance tests showed the robot could move loads up to 1655 g with standard tires and 3340 g with sponge tires. These results highlight the advantages of combining consumer-grade game controllers with advanced microcontrollers, offering improved precision, extended range, and intuitive user interaction for competitive robotics.
Co-Authors Akbar Iskandar Andi Muh Islah Andi Nur Fadillah Andi Patombongi Asrul Billy Eden William Asrul Cakra Cakra Damayanti, Annisa Dwi Etika Vidyarini Feng Zhipeng Feng, Zhipeng Gani, Helmy Gani, Helmy Herlinah Herlinah HS, Hafsah Ibrar Aprianto, Muhammad Jabir, St. Nurhayati Kiyoshi Tomimatsu Luh Putu Ratna Sundari Lutfi Mubarak, Muhammad Muflih Mudinillah, Adam Muhammad Akbar Salcha Muhammad Faisal Munsyir, Mulawarman Najirah Umar Nur Fadhilah Gani Nur, M. Adnan Nurani Nurani Nurani Nurani Nurani Nurani Rejeki, Aisyah Sri Riadnin Maharja Rizky Maharja Sahibu, Supriadi Sidehabi, Sitti Wetenriajeng Sitti Wetenriajeng Sidehabi Sukriadi Sukriadi Syafruddin Syarif Tamsir Taufik, Imran Wardhani, Nurilmiyanti Yuyun Yuyun Yuyun, Yuyun Zhipeng, Feng Zuhriyah, Sitti