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All Journal Jurnal Keteknikan Pertanian Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal ULTIMATICS Jurnal Ilmiah Sinus INTI Nusa Mandiri Paradigma Policy Brief Pertanian, Kelautan, dan Biosains Tropika The Indonesian Journal of Computer Science
Toto Haryanto
Departemen Ilmu Komputer Fakultas Matematika Dan Ilmu Pengetahuan Alam Institut Pertanian Bogor
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Two-Way Thesis Supervisor Recommendation System Using MapReduce K-Skyband View Queries Dasri, Dasri; Annisa, Annisa; Haryanto, Toto
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2800

Abstract

Timely graduation is an important indicator of the quality of higher education. Yet, many students struggle to complete their studies on time due to challenges in finding relevant research topics and suitable supervisors. This study developed a two-way supervisor recommendation system that considers the preferences and expertise of both students and supervisors. The main contribution of this research is the comparison of Block Nested Loop (BNL) k-skyband and MapReduce k-skyband algorithms. The recommendation model developed in this study uses course syllabi to obtain research topics and academic grades to determine students' interests in research topics. A total of 239 research topics were obtained from 37 courses. Optimal recommendations were achieved with a k value of 16. Implementing the MapReduce algorithm in this recommendation model demonstrated a computation speed 8 times faster than the BNL k-skyband approach, making it effective in handling large datasets. The proposed recommendation system received positive feedback from students, with scores of 3.5 for relevance, 3.7 for topic diversity, 3.4 for serendipity, and 3.5 for novelty. These findings suggest that the proposed recommendation system can support students in their research endeavors and improve the overall supervision process in academic settings, with potential for widespread implementation across various study programs. Thus, contributing to the overall improvement of higher education quality.
Classification of Coral Images Using Support Vector Machine with Gray Level Co-Occurrence Matrix Feature Extraction Nababan, Adi Pandu Rahmat; Haryanto, Toto; Wijaya, Sony Hartono
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2708

Abstract

This research developed a coral image classification method using Support Vector Machine (SVM) with Gray Level Co-occurrence Matrix (GLCM) feature extraction to improve the accuracy of coral reef condition monitoring. Coral images were collected in the waters of Sangihe Islands Regency and labelled by experts for healthy, unhealthy, and dead categories. Preprocessing included cropping, background removal, sharpening, and image normalization. GLCM feature extraction was performed with a distance of 1, 2, and 3 pixels and directions of 0°, 45°, 90°, and 135°. SVM uses Linear, Radial Basis Function, and Polynomial kernels with parameters set in a grid. The results indicate that the polynomial kernel with parameters C=10, degree=3, and gamma=1 achieves the highest accuracy, at 91.85%. Oversampling increased the accuracy by 2.17%, while feature selection by boxplot and model-based decreased the accuracy by 0.8% and 0.2%, respectively. On the other hand, feature selection using correlation analysis significantly decreased accuracy by 16.11%. These findings significantly contribute to coral reef conservation by offering a more accurate and efficient classification method. This method enables better and timely monitoring of coral reef conditions, thus supporting more effective conservation interventions. Integrating these research results into IoT systems can improve overall coral reef monitoring and conservation efforts.
SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design
Co-Authors Abdul Latif Aditika, Exciyona Adline Twince Tobing, Fenina Agus Buono Alita Wulan Dini Angga Bayu Santoso Aniati Murni Annisa Annisa Anuraga Jayanegara Arham, Iffah Luthfiyah Asterika Prawesti Azhari, Irmawati Carolina Aziz Kustiyo Budiman Budiman Chrysanti, Rachma Chusyairi, Ahmad Cian Ramadhona Hassolthine Dasri, Dasri Djuara P Lubis Dwi Retno Hapsari, Dwi Retno Endang Purnama Giri Eri Mardiani Eva Rahmawati Eylen Desy Novita Fauziah Fauziah Ganda Wijaya Guritno, Hanif Bagus Hari Agung Adrianto Hartono Wijaya, Sony Hehanussa, Siti Gayatri Herlambang Brawijaya Heru Suhartanto Imam Nawawi Imas Sukaesih Sitanggang Irman Hermadi Irzal Effendi Jamaludin JAMALUDIN Jasni Mohammad Zain Khairani Kusmardi Kusmardi Lidya Ningsih Mahmudi Siwi, Mahmudi Marimin , Muhammad Ikhwani Saputra Muji Yuswanto Mushthofa Nababan, Adi Pandu Rahmat Niken TM Pratiwi Niken TM Pratiwi Noer Muslimah, Meia Nur Hayati Rachmad Nur Hayat Ridwansyah Ridwansyah Rizal Amegia Saputra Rizky Kurniawan RR. Ella Evrita Hestiandari Sahrial Ihsani Ishak Shelvie Nidya Neyman Silvia Rahmi Silvia Rahmi Siti Khotimatul Wildah Sony Hartono Wijaya Sony Muhammad Sri Wahjuni Titania Aulia Tobing, Fenina Adline Twince Tri Widodo Verry Riyanto Viktor Vekky Ronald Repi Wasyianti, Sri Wina Widiati Yusoff, Marina