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All Journal Jurnal Teknik International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JPM (Jurnal Pemberdayaan Masyarakat) ILKOM Jurnal Ilmiah Computer Science and Information Technologies Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Mandiri Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Bandung Conference Series: Industrial Engineering Science Bandung Conference Series: Statistics Bulletin of Social Informatics Theory and Application Journal of Embedded Systems, Security and Intelligent Systems Linier: Literatur Informatika dan Komputer
Manga, Abdul Rachman
Univeristas Muslim Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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SISTEM TRACER STUDY ALUMNI FAKULTAS ILMU KOMPUTER UNIVERSITAS MUSLIM INDONESIA MENGGUNAKAN METODE ON-LINE ANALITYCAL PROCESSING (OLAP) Syam, Aminurlah; Manga, Abdul Rachman
ILKOM Jurnal Ilmiah Vol 9, No 1 (2017)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v9i1.114.86-90

Abstract

Penyimpanan data secara rutin dan terus menerus data alumni Fakultas Ilmu Komputer dapat menimbulkan penumpukan data. Seperti data quesioner alumni yang menjadi salah satu masalah dalam melakukan inputan dikarenakan terkendala jarak. Selama ini pihak fakultas harus mecari data alumni yang telah bekerja atau berada diluar daerah. Sistem yang berbasis website tersebut akan menggunakan data warehouse dan penerapan metode Online Analitycal Processing (OLAP) yang nantinya akan berfungsi sebagai laporan dari data alumni dalam bentuk grafik. Dalam penelitiannya ini data warehouse dirender kedalam metode OLAP yang menghasilkan laporan dalam bentuk grafik. Sistem berbasis website juga memudahkan staff kemahasiswaan dan alumni dalam melakukan inputan karena bisa dilakukan dimanapun dengan adanya koneksi internet
Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor Jumarlis, Mila; Mirfan, Mirfan; Manga, Abdul Rachman
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.910.1-9

Abstract

Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.
Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds Manga', Abdul Rachman; Herawati, Herawati; Purnawansyah, Purnawansyah
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2349.12-19

Abstract

This study aims to develop and evaluate a buffalo breed detection system that supports the cultural practices of the Toraja community, particularly in the context of the Rambu Solo’ ceremony. The ceremony places significant importance on the types of buffaloes used, as each breed symbolizes different social statuses and cultural meanings. In response to the need for an accurate and efficient identification method, this research utilizes the YOLOv9 (You Only Look Once version 9) deep learning model to detect and classify Toraja buffalo breeds. A dataset comprising 2,656 annotated images was used, representing five distinct buffalo categories: bongga sori, bonga ulu, moon, saleko, and todi. The images were collected from both field documentation and online sources. The YOLOv9 model was trained across 90 epochs, aiming to achieve high accuracy in breed detection and classification. The evaluation results demonstrate the model's strong performance, achieving a precision of approximately 0.9 and a recall of 0.8. These metrics indicate the model's ability to correctly identify the buffalo breeds with a high degree of reliability. However, during the training process, certain patterns of overfitting and underfitting were observed, suggesting that the model's performance could still be improved. These issues can potentially be addressed by increasing the volume and diversity of training data, applying data augmentation techniques, and fine-tuning hyperparameters to achieve a more balanced generalization. Overall, the findings show that YOLOv9 is a promising tool for supporting cultural preservation through technology by automating the identification of buffalo types used in traditional ceremonies. This system can assist in maintaining the accuracy and consistency of buffalo classification according to local customs. Future research is recommended to explore broader datasets, compare alternative object detection algorithms, and develop an integrated application for practical field use.
Detection System of Strawberry Ripeness Using K-Means Indra, Dolly; Satra, Ramdan; Azis, Huzain; Manga, Abdul Rachman; L, Harlinda
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1054.25-31

Abstract

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.
Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset Manga', Abdul Rachman; Handayani, Anik Nur; Herwanto, Heru Wahyu; Asmara, Rosa Andrie; Sulistya, Yudha Islami; Kasmira, Kasmira
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1357.186-192

Abstract

Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.
Single-input and multi-input local binary pattern classification Abdul Rachman Manga; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Roesman Ridwan Raja
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2183

Abstract

Identification and classification of species are crucial for maintaining genetic diversity and supporting sustainable agricultural practices. The Toraja Buffalo, a unique type of buffalo in Indonesia, holds high cultural and economic value. Accurate classification of this species is essential to preserving genetic resources and improving breeding programs. Previous studies using single classification methods have shown limitations in complex cases such as the Toraja Buffalo, which has numerous physiological characteristics such as body size, head, horns, tail, and eyes. The purpose of this study is to evaluate and compare the performance of single-classification and multi-category methods for identifying Toraja Buffalo. Several algorithms, including K-Nearest Neighbors (K-NN), Random Forest, Support Vector Machine (SVM), and Naive Bayes, were tested using Local Binary Pattern (LBP) for feature extraction. Decision Tree and others were observed, showing 85.83% accuracy in single-input, while multi-input accuracy reached 92.08%. The multi-input approach consistently improved performance across all algorithms. Multi-input classifiers significantly outperformed single-feature methods, with Random Forest being the most efficient algorithm. Future research could incorporate additional variables such as skin color or genetic profiles to further enhance accuracy.
Implementasi Sistem Layanan Mandiri untuk Efisiensi Administrasi Desa Biji Nangka Kabupaten Sinjai Purnawansyah; Rahma Puspitasari; Abdul Rachman Manga'; Herdianti Darwis; Sitti Nurhalimah
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v11i1.13200

Abstract

This community service program aims to improve administrative efficiency in Biji Nangka Village, which previously used manual processes and was prone to delays, inconsistencies, and the risk of archive loss. This activity implemented a website-based self-service system and provided training to village officials on the use of key features such as digital letter management, automatic numbering, and electronic archive storage. A total of 17 participants participated in the training and all successfully operated the system. Evaluation results showed that the time to create letters was reduced from 10–15 minutes to 3–5 minutes. Furthermore, the results of the pre-test and post-test comparison showed a 9.412% increase in participant understanding, indicating the effectiveness of the training in improving the digital competence of village officials. Overall, this program has had a positive impact on improving the quality of administrative services and supporting the realization of digital-based village governance.
Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset Poetri Lestari Lokapitasari Belluano; Reyna Aprilia Rahma; Herdianti Darwis; Abdul Rachman Manga
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p235-242

Abstract

This study aims to analyze the performance of various ensemble machine learning methods, such as AdaBoost, bagging, and stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that AdaBoost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with AdaBoost being an exception that shows a decrease in accuracy after applying imbalanced data methods.
Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets Abdul Rachman Manga; Aulia Putri Utami; Huzain Azis; Yulita Salim; Amaliah Faradibah
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p205-214

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.
Perancangan Sistem Kendali Otomatis Pemberian Pakan Pada Ayam Broiler Dengan Pengaturan Waktu Terjadwal Muhammad Alif Ambas; Dedy Atmajaya; Abdul Rachman Manga’
LINIER: Literatur Informatika dan Komputer Vol 3, No 2 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v3i2.3638

Abstract

Pemberian pakan ayam broiler secara manual kerap dianggap tidak efisien karena memerlukan waktu, tenaga, dan berpotensi mengganggu konsistensi jadwal pemberian pakan. Penelitian ini bertujuan merancang sistem otomatisasi pemberian pakan berbasis mikrokontroler Arduino ATmega328 SMD yang bekerja secara terjadwal untuk meningkatkan efisiensi operasional peternakan. Sistem ini dilengkapi dengan sensor ultrasonic untuk memantau ketinggian pakan, load cell untuk menimbang berat pakan, modul Real Time Clock (RTC) untuk mengatur waktu, LCD sebagai penampil informasi, serta motor servo untuk mengatur buka-tutup katup pakan. Pemberian pakan dilakukan dua kali sehari, yakni pukul 08.00 dan 17.00, dengan takaran antara 315 hingga 393 gram per sesi. Simulasi pengujian menunjukkan bahwa semua komponen berfungsi optimal dalam skenario otomatis. Hasil perancangan sistem membuktikan bahwa sistem bisa digunakan dan diimplementasikan bagi peternak ayam untuk mengurangi beban kerja peternak, menjaga konsistensi waktu pemberian pakan. Dengan demikian, rancangan ini menawarkan solusi efektif dan praktis dalam manajemen pemberian pakan pada ayam broiler secara otomatis dengan pengaturan waktu terjadwal
Co-Authors Achrul Abdullah Admojo, Fadhila Tangguh Adriawan Amrullah Ali Munawir Aliefian Raflisyah Amaliah Faradibah Andi Anugrah Aqsa Andi Azizul Hidayat Andi Muh Afdal Andri Rajsya Anik Nur Handayani Anik Nur Handayani Asman Haris Aulia Putri Utami Aziz, Huzain Chaerun Niam Syah Dedy Atmajaya Dedy Atmajaya Dewi Widyawati Dolly Indra Dwiky Aries Nugraha Yusuf Fadlan Hasan Fadly Shabir Fahmi Faradibah, Amaliah Farniwati Fattah Harlinda Lahuddin Herawati Herawati Heru Wahyu Herwanto Heru Wahyu Herwanto Hilal Luthfi Hibatullah Huzain Azis Huzain Azis Huzain Azis Ihwana As’ad Irawati Irawati Jalil, Rizqi Ananda JUMARLIS, MILA Kasmira, Kasmira Khalish Ghandur Syamsuddina L, Harlinda lilis nurhayati Listyan Nur Saida Lokapitasari Belluano, Poetri Lestari Mufti, Farid Wajdi Muh Fatwah Fajriansyah M Muh Fatwah Fajriansyah Marlang Muh Syawal Muh Yeyen Dwi Suherman Muhammad Alif Ambas Muhammad Arfah Asis Muhammad Arzhi Azis Muhammad Fiqram Muis, Ismunandar Nabila Vita Dewi Nanda, As'syahrin Nasir, Haidawati Nurhalima Nurhalima Purnawansyah Purnawansyah Rahma Puspitasari Rahma Puspitasari Rahmat A Ramdan Satra Reyna Aprilia Rahma Roesman Ridwan Raja Rosa Andrie Asmara Salim, Yulita Samsul Shabir, Fadly Siska Anraeni Siti Rahmi Kelilauw Sitti Nurhalimah Subhan Ardhiman Syafie, Lukman Syam, Aminurlah Syam, Aminurlah Syamsul Bahri Syamsumarlin Syamsumarlin Tasrif Hasanuddin Tenri Sa'nah Tri Anita Resky Ramadhani Utami, Aulia Putri Wahyu Kadri Rahmat Suat Suat Yudha Islami Sulistya Yulita Salim Yusrizal Damri Zulham Jaya Syafar