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All Journal Jurnal Simetris Register: Jurnal Ilmiah Teknologi Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JTT (Jurnal Teknologi Terpadu) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) Jambura Geoscience Review Jambura Journal of Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Journal La Sociale Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bureaucracy Journal : Indonesia Journal of Law and Social-Political Governance Jurnal Teknologi Informasi Cyberku Jurnal Ilmu Komputer Jurnal Sains dan Teknologi Prohealth Journal Jurnal Ilmu Komputer, Teknologi Dan Informasi
Mohamad Ilyas Abas
Universitas Muhammadiyah Gorontalo
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Analisis Persepsi Pengguna Jalan terhadap Penerapan Sistem Satu Arah: Studi Kasus Jalan Nani Wartabone, Gorontalo Ahmad, Noor Fatmawanti; Abas, Mohamad Ilyas
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3640

Abstract

Traffic congestion in educational areas is a persistent issue in medium-sized Indonesian cities, including Gorontalo. JalanNani Wartabone, adjacent to Gorontalo State University, frequently experiences congestion due to student mobility,vehicle flow, pedestrians, and street vendors. To mitigate this, the city government introduced a one-way traffic system, yetits effectiveness from users’ perspectives has not been fully evaluated. This study examines road user perceptions acrossfive dimensions: comfort, safety, efficiency, accessibility, and satisfaction. Data were collected from 200 respondents(students, pedestrians, drivers, vendors) using questionnaires and analyzed with SPSS through descriptive statistics, crosstabulations, and correlation analysis, complemented by spatial heatmaps from geotagged feedback. Results revealsignificant group differences: students and pedestrians perceived positive effects in traffic order and walkability, whiledrivers and vendors reported reduced accessibility and longer travel times. The study contributes a user-centered,evidence-based framework for inclusive traffic policy in secondary cities
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.
Efektivitas Penggunaan TikTok sebagai Media Edukasi terhadap Pengetahuan Remaja dalam Persiapan Pernikahan Yulianita, Fani; Hiola, Fidya; Umar, Siskawati; Abas, Mohamad Ilyas
ProHealth Journal Vol 22 No 2 (2025): December
Publisher : STIKes Hamzar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59802/phj.2025222193

Abstract

Marriage is one of the important steps in human life that binds men and women as husband and wife. There are regulations that setthe ideal age limit for marriage. However, there is still a high rate of early marriage dispensation. In line with technological developments, social media has become a means of information and education, TikTok is a social media platform that is trendingamong adolescents and has great potential as a means of education. This study aims to determine the effectiveness of using TikTok as an educational medium for adolescent knowledge in preparing for marriage at SMA Negeri 1 Gorontalo. This type of research is quantitative research using Quasi-experimental with a one group pretest-posttest design. The research sample was 41 class XII students at SMA Negeri 1 Gorontalo who were selected using the purposive sampling technique. The instrument used was a pretestand posttest questionnaire that had been tested for validity and reliability. Data analysis was carried out univariately and bivariately using the Wilcoxon Signed rank test. The results of the study showed a significant increase in adolescent knowledge after being given education through TikTok videos for six days, with a significance value of p = 0.000 (<0.05). It can be concludedthat TikTok media is effective as a means of education in increasing adolescent knowledge about marriage preparation. It is hoped that this study can be considered as a premarital education strategy in marriage preparation.
Estimating Urban Land Surface Temperature Using Spatial Machine Learning in Gorontalo City Koto, Arthur Gani; Abas, Mohamad Ilyas; Syahrial, Syahrial; Suparwata, Dewa Oka
Jambura Geoscience Review Vol 8, No 1 (2026): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jgeosrev.v8i1.34308

Abstract

Urban expansion in tropical cities significantly alters surface thermal conditions, intensifying the urban heat island (UHI) phenomenon. This study aims to estimate and analyze the spatiotemporal dynamics of land surface temperature (LST) in Gorontalo City from 1995 to 2025 using a spatial machine learning (SML) approach based on the Random Forest (RF) algorithm. Multitemporal Landsat 5, 7, 8, and 9 images were processed in Google Earth Engine (GEE) to derive surface reflectance, Normalized Difference Vegetation Index (NDVI), emissivity, and brightness temperature, which were subsequently employed as predictor variables in the LST model. A total of 50 ground validation points were used to assess model performance. The RF model achieved high predictive accuracy with an R² of 0.833, RMSE of ±3.33 °C, and MAE of ±2.80 °C, outperforming conventional NDVI-based models. The long-term analysis revealed a consistent increase in LST across urbanized zones, particularly in the city center and northern districts, while areas with higher vegetation cover exhibited lower LST values. The negative correlation between NDVI and LST (R² = 0.3132) confirms the critical role of vegetation in mitigating urban thermal intensity. These findings highlight the applicability of the RF-based SML framework for accurate LST estimation and urban climate monitoring, providing a scientific basis for sustainable urban planning and green infrastructure development in tropical cities.
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.
APPLE FRUIT QUALITY DETECTION (GOOD AND ROTTEN) USING THE YOLOV5 METHOD Fathir Adisyar; Mohamad Ilyas Abas; Widya Eka Pranata; Rizal Lamusu; Syahrial Syahrial; Irawan Ibrahim
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.v6i1.5539

Abstract

Fruit quality is an important factor that affects nutritional value, consumption safety, and market value of agricultural products. Apples, as one of the most widely consumed fruits, are prone to quality degradation due to spoilage, which is often difficult to accurately identify through human visual observation. Manual sorting of apples is subjective, time-consuming, and prone to errors. Therefore, this study aims to develop an automatic apple quality detection and classification system using the You Only Look Once version 5 (YOLOv5) deep learning method. Apple quality is classified into two categories, namely fresh apples and rotten apples, based on digital images. The dataset used in this study consists of 4,035 images obtained from the Roboflow platform, comprising 2,925 training images, 707 validation images, and 403 testing images. All images were resized to 640 × 640 pixels without data augmentation. The model was trained for 50 epochs using GPU acceleration on Google Colab. Model performance was evaluated using a confusion matrix on the testing dataset. The experimental results show that the YOLOv5 model successfully classified all testing images correctly without any misclassification, indicating excellent detection and classification performance. These results demonstrate that YOLOv5 is an effective and reliable method for automatic apple quality detection and has strong potential for application in agriculture and the food industry to improve efficiency and accuracy in fruit quality inspection.
Penerapan Metode Convolutional Neural Network pada Identifikasi Wajah Mahasiswa didalam Ruang Perkuliahan Rahman M. Abdullah; Mohamad Ilyas Abas; Syahrial Syahrial
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.271

Abstract

Manual student attendance systems still present several limitations, including the potential for data manipulation, human error, and low efficiency in large classroom environments. This study aims to implement the Convolutional Neural Network (CNN) method to simultaneously identify students’ faces within a classroom setting. The dataset consisted of 1,740 facial images collected from 58 students using a 2K Full HD webcam under varying capture angles and lighting conditions. The research stages included data collection, image preprocessing, data augmentation, CNN model training, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score metrics. The developed CNN model, named FACENET V5, was designed using TensorFlow with three convolutional blocks, batch normalization, max pooling, dropout, and a softmax classifier. Experiments were conducted using image sizes of 100×100, 200×200, 300×300, and 400×400 pixels with several dataset split scenarios. The results demonstrated that the 100×100 image size with a 90:10 data split achieved the best performance, obtaining a validation accuracy of 98.28% and a loss value of 0.1127. Furthermore, FACENET V5 was compared with ResNet50V2, MobileNetV2, and VGG16. Comparative results indicated that FACENET V5 provided the most optimal performance in simultaneous student face recognition. This study confirms that CNN can be effectively implemented as an automated face recognition-based attendance system in academic environments.
Co-Authors Abdul Syukur Alter Lasarudin Antupetu, Rastin Asminar Asminar Chandra, Muhammad Ali Daulima, Mohamad Rizki Deny Wiria Nugraha Dewa Oka Suparwata Fathir Adisyar Fauziah Fauziah Gani, Hilmansyah Handayani, Tri Pratiwi Hasto Finanto Hasyim, Wahyudin Hidayat, Rahmat Rian Hiola, Fidya Ibrahim, Irawan Ingrid Nurtanio Ismet Hadi Isminarti, Isminarti Kango, Riklan Kiayi, Verliana Koto, Arthur Gani Lamusu, Rizal Lasarudin, Alter Lasarudin, Alter Mahendra, Bachtiar Isra Maku, Rubiyanto Masri, Muhamad Moch Arief Soeleman Moh. Nasrul Arief Setiawan Adam Mohamad, Irwansyah Reza Muhammad Raidinoor Pasha Mulyadi, Yahya Bin Muslim Panigoro Noor Fatmawanti Ahmad Noviyanti, Upik Dyah Eka Nur Azhary Iriawan Eka Putra Nurhikmah Fajar Nursetiawati Nursetiawati Nurwahidah Jamal Panigoro, Muslim Polapa, Risman Pranata, Widya Eka Rahman M. Abdullah Rahmatia Mohi Rizal Lamusu Rizal Lamusu Rizal Lamusu Salahudin Pakaya Silvana Yusuf Sulistyawati, Ni Kadek Syafruddin Syarif SYAHRIAL SYAHRIAL Syahrial Syahrial Syahrial Syahrial Syahrial Syahrial Tangahu, Nur'aini Mufaidhah Tri Pratiwi Handayani Umar Sako, Umar Umar, Siskawati Wahyudin Hasyim Widya Eka Pranata Yulanda Yunus Yulianita, Fani Zulkarnain Suronoto Zulkifli Tahir