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All Journal Jurnal Simetris Sinkron : Jurnal dan Penelitian Teknik Informatika JRST (Jurnal Riset Sains dan Teknologi) Jambura Journal of Informatics J SIG (Jurnal Sains Informasi Geografi) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Hello World Jurnal Ilmu Komputer Journal of Information System, Technology and Engineering
Ibrahim, Irawan
Universitas Muhammadiyah Gorontalo
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The Implementation of MOORA method in the Selection of Direct Cash Aid Recipients Tri Pratiwi Handayani; Irawan Ibrahim; Hilmansyah Gani; Moh. Nasrul Arief Setiawan Adam; Mohamad Ilyas Abas
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4527

Abstract

This research aims to implement the Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) algorithm as a Decision Support System for selecting recipients of Direct Cash Assistance in Cempaka Putih village, Gorontalo. With a dataset of 112 prospectus recipients, the study focuses on developing an efficient approach to assist the village head in the beneficiary selection process. By combining multi-objective optimization and ratio analysis, the MOORA algorithm objectively evaluates and ranks recipients based on eligibility and suitability. The findings demonstrate the effectiveness of MOORA in streamlining the selection process, ensuring transparency and optimizing resource allocation for those most in need. This research contributes to decision support systems by showcasing the practical application of MOORA, enhancing assistance distribution, and improving community welfare. The results show that Alternative A1 receives the highest ranking, which is 1, with a Yi value of 1.32. Therefore, Alternative A1 is recommended as the best candidate to receive direct cash assistance in the Cempaka Putih village. The method has the capability to rank the top 12 suitable candidates who are eligible to obtain direct cash aid. However, there are instances where certain Yi values match, resulting in the same ranking for those alternatives. This similarity necessitates further observation and analysis.
ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT Abas, Mohamad Ilyas; Ibrahim, Irawan; Syahrial, Syahrial; Lamusu, Rizal; Baderan, Umar Sako; Kango, Riklan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11861

Abstract

This study aims to analyze the growth trend of covid-19 using prediction algorithms in data mining for covid-19 data throughout Indonesia. This can be used as a decision support to analyze several government policies towards regulatory intervention so far. The method used is the best prediction method in time series data, including Neural Network, SVM, Linear Regression, K-Neirest Neighborn and optimizes it with optimization algorithms. This research is focused on the application of these applications. It is hoped that this research will produce an analysis of the growth trend of Covid cases every day, in addition to its contribution so that it can assist the government in determining the best policy direction and also as an education to the public. in addition, the research will contribute to science in the field of predictive analysis by finding the best RMSE formulation. The results of this study show that Neural Network-Particle Swarm Optimization has the smallest Roort Mean Square Error which is 265,326, and the two Neural Network Genetic Algorithm are 266.801, Neural Network Forward Selection is 275,372 and Neural Network without optimization has the largest RMSE which is 297.204. These results can be used as a reference for the use of similar algorithms in time series data, both Covid-19 data and other data.
KONSTRUKSI ALGORITMA PEWARNAAN TITIK PELANGI PADA GRAF POHON Pranata, Widya Eka; Abas, Mohamad Ilyas; Ibrahim, Irawan; Lamusu, Rizal; Syahrial, Syahrial
Jurnal Ilmu Komputer (JUIK) Vol 5, No 1 (2025): February 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i1.3839

Abstract

This study constructs an algorithm for rainbow vertex coloring in tree graphs. A graph G is defined as a set pair G=(V,E), where V is the vertex set and E is the edge set. Rainbow dot coloring aims to assign a color to each vertex of a tree graph T, such that each path in the tree has vertices with unique colors. The rainbow dot coloring algorithm developed in this study is implemented in a programming language, and its performance is evaluated through simulations on various types of tree graphs. The results show that this algorithm can effectively color tree graphs with good optimality. This research contributes to graph coloring theory and its potential to be applied to computational problems involving tree graphs with complex structures.
KLASIFIKASI KUALITAS TELUR BERDASARKAN KERABANG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Daulima, Mohamad Rizki; Syahrial, Syahrial; Abas, Mohamad Ilyas; Pranata, Widya Eka; Ibrahim, Irawan
Jurnal Ilmu Komputer (JUIK) Vol 5, No 1 (2025): February 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i1.3953

Abstract

Telur ayam merupakan salah satu bahan pangan yang banyak digemari oleh masyarakat dan memiliki kandungan nutrisi yang sangat dibutuhkan tubuh. Kualitas telur sangat dipengaruhi oleh faktor internal dan eksternal, yang dapat memengaruhi kesegarannya. Penurunan kualitas telur sering disebabkan oleh kerusakan fisik, kontaminasi mikroba, serta kondisi penyimpanan yang tidak tepat. Salah satu bentuk kerusakan yang umum adalah keretakan pada kerabang telur, yang dapat membuka celah bagi masuknya bakteri berbahaya seperti Salmonella. Oleh karena itu, penting untuk melakukan identifikasi kualitas telur secara akurat, terutama untuk mencegah kerugian ekonomi pada peternak dan menjaga kesehatan konsumen. Salah satu metode yang efektif untuk mengidentifikasi kualitas telur adalah dengan menggunakan teknologi pengolahan citra digital, khususnya melalui penerapan Convolutional Neural Network (CNN). Penelitian ini bertujuan untuk mengembangkan dan menerapkan metode CNN dalam mengklasifikasikan kualitas telur ayam berdasarkan kondisi kerabangnya. Dengan menggunakan citra digital yang diambil melalui kamera handphone, diharapkan penelitian ini dapat memberikan solusi praktis dalam klasifikasi kualitas telur, baik untuk konsumsi maupun distribusi di pasar. Hasil yang diharapkan dari penelitian ini adalah tercapainya tingkat akurasi yang tinggi dalam identifikasi kualitas telur, yang dapat mempermudah proses seleksi telur oleh peternak dan konsumen.
REPOSITORY PERPUSTAKAAN BERBASIS ANDROID DI UNIVERSITAS MUHAMMADIYAH GORONTALO Lamusu, Rizal; Lasaruddin, Alter; Ibrahim, Irawan; Lestari, Gita
Jurnal Ilmu Komputer (JUIK) Vol 3, No 1 (2023): Februari 2023
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v3i1.2000

Abstract

This research was conducted with the aim of developing the Repository application, initially the system was more directed to journal searches, did not provide reading access rights, no journal download feature and the information was still incomplete so that it would be developed using Android-based technology. Through this research, it is hoped that the journal search process wil be much more this application was developed using the SDLC system development method or Software Development Life Cycle which has several stage, namely Planning, Analysis, Design and Implementation. By using system testing Black-box type Boundry Value Analysis/Limit Testing. The result of this research are in the form of an Android-based library repository application software.
Repositori Berbasis Web Fakultas Sains Dan Ilmu Komputer Universitas Muhammadiyah Gorontalo Lamusu, Rizal; Maku, Rubiyanto; Ibrahim, Irawan; Gobel, Alvian Van
Jurnal Ilmu Komputer (JUIK) Vol 4, No 2 (2024): JUNE 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i2.3278

Abstract

The Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo is a higher education entity that has various needs in managing and utilizing data, information and important documents. This research aims to design a web-based repository for the Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo and implement a web-based repository at the Faculty of Science and Computer Science, Muhammadoyah University of Gorontalo. This research was developed using a prototype model, PHP programming language, Laravel framework, MySQL and system design tools using Unified Modeling Language. The results of research using a prototype system development model show that the Web-Based Repository System, Faculty of Science and Computer Science, Muhammadiyah University, Gorontalo has reached the maturity level. which allows its effective use. With this system, it is hoped that employees of the Head of Administration and his staff at the Faculty of Science and Computer Science, Muhammadiyah University of Gorontalo can implement a web-based repository that is effective, efficient, and in accordance with academic and administrative
Implementation of the Profile Matching Method in a Football Player Position Decision Support Handayani, Tri Pratiwi; Poha, Rahmad A; Ibrahim, Irawan; Ghani, Hilmasyah
Journal of Information System, Technology and Engineering Vol. 2 No. 2 (2024): JISTE
Publisher : Yayasan Gema Bina Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61487/jiste.v2i2.73

Abstract

This study aims to overcome this issue by objectively determining the best positions of the soccer players through the application of the Profile Matching technique. The process consists of five steps: GAP values are first determined by comparing each player's unique traits to predetermined benchmarks. Next, compute the primary and secondary standards, establish weighted scores by utilizing the GAP values, compute the GAP values by contrasting specific player attributes with predetermined benchmarks, compute the overall scores, and order players according to their performance. A weighted combination of mental, physical, and skill criteria (30, 40%, and 30%, respectively) determines the final positions. The results demonstrate how effectively this rigorous technique scores the participants. This process offers a logical and objective approach to player selection, enhancing the precision and effectiveness of decision-making in soccer team management.
Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Abas, Mohamad Ilyas; Lamusu, Rizal; Pranata, Widya Eka; Syahrial, Syahrial; Ibrahim, Irawan; Hasyim, Wahyudin; Kiayi, Verliana
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Optimasi Support Vector Machine Particle Swarm Optimization Untuk Prediksi Konsumsi Energi Listrik Abas, Mohamad Ilyas; Ibrahim, Irawan
Jambura Journal of Informatics VOL 1, NO 2: OCTOBER 2019
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1055.676 KB) | DOI: 10.37905/jji.v1i2.2646

Abstract

Penelitian ini bertujuan untuk menganalisis konsumsi energi listrik di Gorontalo dan melakukan prediksi terhadap penggunaan energi listrik. Konsumsi dan beban listrik di Gorontalo menjadi pokok bahasan dalam penelitian ini. Metode yang digunakan dalam melakukan prediksi yakni SVM dan Optimasi PSO. Algoritma ini dipilih karena memiliki nilai akurasi yang tinggi dengan tingkat error yang rendah. Hasil dari penelitian ini menunjukkan bahwa SVM-PSO mampu melakukan prediksi dengan data time-series dengan error yang kecil. Selain itu, hasil dari penelitian ini dapat digunakan untuk mempersiapkan pasokan listrik jangka panjang serta dapat mensosialisasikan penggunaan listrik yang baik kepada masyarakat. Energi alternatif juga dapat menjadi solusi bagi pemerintah guna menambah pasokan energi listrik sehingga kebutuhan masyarakat akan listrik dapat terpenuhi. This study aims to analyze the consumption of electrical energy in Gorontalo and make predictions on the use of electrical energy. Electricity consumption and load in Gorontalo is the subject of this research. The method used in making predictions is SVM and PSO Optimization. This algorithm was chosen because it has a high accuracy value with a low error rate. The results of this study indicate that SVM-PSO is able to make predictions with timeseries data with small errors. In addition, the results of this study can be used to prepare long-term electricity supply and can socialize good use of electricity to the public. Alternative energy can also be a solution for the government to increase the supply of electrical energy so that people's needs for electricity can be met.
Avocado Ripeness Classification Using a Convolutional Neural Network (CNN) Tangahu, Nur'aini Mufaidhah; Abas, Mohamad Ilyas; Pranata, Widya Eka; Lamusu, Rizal; Syahrial, Syahrial; Ibrahim, Irawan
Jurnal Ilmu Komputer (JUIK) Vol 6, No 1 (2026): February 2026
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v6i1.5540

Abstract

The determination of avocado ripeness plays a crucial role in post-harvest handling and quality The determination of avocado ripeness plays a crucial role in post-harvest handling and quality control within the agricultural sector; however, conventional assessment methods based on visual inspection and human experience are often subjective and inconsistent, potentially leading to classification errors and economic losses. To address this issue, this study proposes an automated avocado ripeness classification system using a Convolutional Neural Network (CNN) based on digital image analysis. The model employs a transfer learning approach using the MobileNet architecture implemented through the Teachable Machine platform. The dataset utilized in this research was obtained from Mendeley Data and consists of avocado images categorized into four ripeness levels: underripe, breaking, ripe, and overripe. Prior to model training, the images underwent preprocessing and data augmentation to improve model robustness and generalization. Model evaluation was conducted using 1,200 test images, with 300 samples per class. Experimental results show that the proposed model achieved an overall accuracy of 91.42%, indicating strong and stable classification performance. Analysis using a confusion matrix reveals that most predictions were correctly classified, while misclassifications primarily occurred between ripeness stages with visually similar characteristics. Among all classes, the underripe category demonstrated the highest performance with minimal classification errors. These findings indicate that the proposed CNN-based approach is effective and reliable, and it has significant potential to be further developed as an automated system for avocado ripeness classification and post-harvest quality assessment.
Co-Authors Alter Lasarudin Daulima, Mohamad Rizki Fathir Adisyar Gani, Hilmansyah Ghani, Hilmasyah Gobel, Alvian Van Handayani, Tri Pratiwi Kango, Riklan Kiayi, Verliana Lamusu, Rizal Lasaruddin, Alter Lestari, Gita Maku, Rubiyanto Masri, Muhamad Moh. Nasrul Arief Setiawan Adam Mohamad Ilyas Abas Muslim Panigoro Nursetiawati Nursetiawati Panigoro, Muslim Poha, Rahmad A Pranata, Widya Eka Pratiwi I Wantu Pratiwi I Wantu Pratiwi I Wantu Rizal Lamusu Rizal Lamusu Rubiyanto Maku Salahudin Pakaya SYAHRIAL SYAHRIAL Syahrial Syahrial Syahrial Syahrial Tangahu, Nur'aini Mufaidhah Tri Pratiwi Handayani Tri Pratiwi Handayani Umar Sako, Umar Wahyudin Hasyim Widya Eka Pranata Yulan Humolungo Yulanda Yunus