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All Journal International Journal of Electrical and Computer Engineering Buletin Peternakan JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Indonesian Journal of Statistics and Its Applications Warta Penelitian Perhubungan
Sony Hartono Wijaya
Departement Of Computer Science, Faculty Of Animal Science, IPB University, Bogor, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mathematics Transportation Veterinary Other

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Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2093-2100

Abstract

Computer vision deals with image-based problems, such as deep learning, classification, and object detection. This study classifies the quality of dairy parents into three, namely high, medium, and low based on morphology by focusing on Bogor Indonesia farms. The morphological images used are the side and back of dairy cows and the challenge is to determine the optimal accuracy of the model for it to be implemented into an automated system. The 2-step mask region-based convolutional neural network (mask R-CNN) and Canny segmentation algorithm were continuously used to classify the convolutional neural network (CNN) in order to obtain optimal accuracy. When testing the model using training and testing ratios of 90:10 and 80:20, it was evaluated in terms of accuracy, precision, recall, and F1-score. The results showed that the highest model produced an accuracy of 85.44%, 87.12% precision, 83.79% recall, and 84.94% F1-score. Therefore, it was concluded that the test result with 2-stages of segmentation was the best.
Mapping and predicting research trends in international journal publications using graph and topic modeling Asep Herman Suyanto; Taufik Djatna; Sony Hartono Wijaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1201-1213

Abstract

Researchers and journal managers need summary information, such as research maps and trends. Topic and words-based document content analysis alternative to science mapping and trend prediction based on bibliographic analysis. The data are a collection of journal articles/proceeding documents and metadata for 2011-2020 published by the International Journal of Electrical and Computer Engineering (IJECE). A combination of several techniques and methods is used, such as text mining, topic modeling, cosine similarity, network analysis, graph theory, and seasonal autoregressive integrated moving average (SARIMA). This research has produced research topics, mapping, trend prediction, and visualization according to its objectives. The results of the topic coherence test obtained the optimal number of topics, as many as 6. The results of the topics were evaluated by experts to be given labels and areas of focus. On the research map for each topic, information is found on trending research, the most popular research, research that is central to the research group, and critical research on the development of the group path. It also identifies the type of breakthrough, incremental, and research gap. Predictions of research trends obtained are based on topics and words that describe the development of research. Visualization is descriptive and predictive.
Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework Adhi Kusnadi; Yandra Arkeman; Khaswar Syamsu; Sony Hartono Wijaya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26443

Abstract

The demand for halal products in the Islamic context continues to be high, requiring adherence to halal and haram laws in consuming food and beverages. However, individuals face the challenge of distinguishing between haram meat and permissible halal meat. This study aims to answer these challenges by designing an expert system application within the ERP framework to increase the usability functionality of the system that can differentiate between beef, pork, or a mixture of both based on the physical characteristics of the meat. The aim is to determine halal products permissible for consumption by Muslims. The research methodology includes a data collection process that involves taking 30 meat samples from various sources, and the criteria used to classify the meat will be determined based on an analysis of the physical characteristics of the meat. System administrators use expert systems to ensure proper treatment of meat during administration processes, including separating halal beef from pork and implementing different inventory procedures. The Certainty Factor (CF) inference engine deals with uncertainty even though the expert system's accuracy level is relatively good with several rules. However, these results must be studied further because the plan relies on expert opinion. Therefore, it is necessary to set the correct CF value for accurate height classification. The CF inference engine facilitates reasoned conclusions in meat classification. Functional testing confirms the smooth running of the system, validating its reliability and performance. In addition, the expert system accuracy assessment produces a commendable accuracy rate of 90%. In addition, the expert system works powerfully on various meat samples, accurately classifying meat types with high precision. This study explicitly highlights the expert system's design for meat classification in determining halal products by using the Expert System Certainty Factor. In conclusion, this expert system provides an efficient and reliable approach to classifying meat and supports the production and consumption of Halal products according to Islamic principles.
Pengelompokan Publikasi Ilmiah Berdasarkan Bidang Kepakaran Menggunakan Latent Dirichlet Allocation dan Normalized PSO-K-means Hayatina, Fina Charisma; Wijaya, Sony Hartono; Hardhienata, Medria Kusuma Dewi
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.121-132

Abstract

Salah satu cara untuk memvalidasi klaim kepakaran dosen adalah dengan meninjau dokumen publikasi ilmiah yang tersedia. Namun, menentukan kelompok kepakaran dari sejumlah dokumen memerlukan pengetahuan yang memadai dan waktu yang relatif lama, sehingga menjadi sulit dilakukan. Penelitian ini bertujuan untuk membangun suatu model yang dapat mengelompokkan dokumen berdasarkan bidang kepakaran. Penelitian ini menggunakan algoritma klasterisasi K-means untuk mengelompokkan dokumen berdasarkan bidang kepakaran dosen. Latent dirichlet allocation digunakan untuk mereduksi dimensi data, dan particle swarm optimization digunakan untuk menentukan centroid awal pada algoritma K-means. Hasil penelitian ini berhasil mengelompokkan dokumen publikasi ilmiah dengan nilai koefisien silhouette sebesar 0.42. Selain itu, penggunaan PSO sebagai penentu centroid optimal pada algoritma K-means dapat meningkatkan nilai koefisien silhouette sebesar 5.56%. Model yang dibangun dievaluasi dengan mencocokkan klaster yang dihasilkan dengan klaim yang diberikan. Hasilnya menunjukkan bahwa sebanyak 75% hasil pencocokan sesuai dan 25% tidak sesuai.
Model Klasifikasi Fase Pertumbuhan Tebu dari Citra Sentinel 1 Multi-temporal Menggunakan Algoritma Random Forest Bramdito, Vandam Caesariadi; Wijaya, Sony Hartono; Sitanggang, Imas Sukaesih
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.212-223

Abstract

The Special Region of Yogyakarta, a designated sugarcane center, demands special attention for effective extensification efforts, necessitating spatial insights into sugarcane farming. Monitoring of sugarcane fields served to obtain information on the growth phases of sugarcane and its distribution for agricultural extensification strategies. For this reason, it is necessary to carry out image classification using the Random Forest reliable algorithm to classify sugarcane growth phases in multi-temporal Sentinel 1 images. The sugarcane planting calendar Map is conducted from the image classification outcomes and then tested for its accuracy for evaluation. The classification process involves analyzing each image captured monthly throughout 2020, with a dataset comprising 9690 sample pixels across six classification classes: buildings, vegetation, water bodies, rice fields, sugarcane phase class 1, and sugarcane phase class 2. The results show that the Sentinel 1 image consisting of 13 images has an average classification model accuracy of 65.38%. Notably, the image classification achieved its pinnacle performance in October, boasting the highest overall accuracy level at 73.33%, accompanied by an RMSE value of 2.05.
ENHANCING UNDERWATER IMAGE QUALITY: EVALUATING COMBINATIVE APPROACHES FOR EFFECTIVE IN SEAGRASS BED ECOSYSTEM Sri Dianing Asri; Indra Jaya; Agus Buono; Sony Hartono Wijaya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5566

Abstract

The Complex underwater characteristics, challenges for image processing tasks. These images often have poor visibility due to low contrast, light scattering and various types of interference. There is a lack of exploration into the effectiveness of existing underwater image enhancement methods, particularly in the context of seagrass ecosystems, allows for further investigation. This study aims to explore and evaluate the effectiveness of various methods in underwater image enhancement, including Colour Balanced, CLAHE, and Unsharp Masking and their combinations, starting with converting video data from UTS devices into two-dimensional images. Furthermore, the quality of images taken from underwater cameras placed in a complex and wild seagrass meadow environment was improved using the proposed method, and the quality was evaluated by the SSIM value. The results show that the CLAHE method has the highest average SSIM value of 0.898. Meanwhile, the combined Color Balanced-CLAHE method achieved an SSIM value of 0.683 in a separate evaluation. This combination is an innovative approach to address complex underwater image quality problems, providing a more specific and adaptive solution. Overall, the proposed method is able to improve the visual quality of images on aspects such as clarity, color, and visibility of objects in the image
The Development of Classification Algorithm Models on Spam SMS Using Feature Selection and SMOTE Chrysanti, Rachma; Wijaya, Sony Hartono; Haryanto, Toto
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.v16i3.2220.356-370

Abstract

Short Message Service (SMS) is a widely used communication media. Unfortunately, the increasing usage of SMS has resulted in the emergence of SMS spam, which often disturbs the comfort of cellphone users. Developing a classification model as a solution for filtering SMS spam is very important to minimize disruption and loss to cellphone users due to SMS spam. To address this issue, utilize the Naïve Bayes algorithm and Support Vector Machine (SVM) along with Chi-square and Information Gain. This study focuses on the classification and analysis of SMS spam on a cellular operator service in a telecommunications company using machine learning techniques. This study applies and combines a combination of classification methods including Naive Bayes and Support Vector Machine (SVM). The combination is carried out with Chi-square and Information Gain feature selection to reduce irrelevant features. This study also applies a combination with data balancing techniques using the Synthetic Minority Oversampling Technique (SMOTE) to balance the number of unbalanced classes. The results show that SMOTE improves classification performance. SVM performs spam SMS classification or not spam Model 7 (SVM) achieves accuracy 98,55% and it has improved the performance when it was combined with SMOTE Model 10 (SVM + SMOTE) achieves F1-score 99,23% in performing spam SMS classification or not this outperforms all other models. These results indicate that the SVM algorithm achieved better performance in detecting spam SMS compared to Naive Bayes, which demonstrated a lower level of accuracy. These results illustrate the effectiveness of combining machine learning models to enhance classification accuracy with balanced data, emphasizing the model that exhibited the most substantial improvement in performance.
SAE-DNN-GA: Sebuah Pendekatan Klasifikasi Multilabel dalam Prediksi Senyawa Herbal Potensial Untuk Penyakit COVID-19 Wijaya, Eko Praja Hamid; Kusuma, Wisnu Ananta; Wijaya, Sony Hartono
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.111-121

Abstract

COVID-19 adalah penyakit dengan laju penyebaran yang tinggi. Percepatan proses penemuan obat untuk penyakit tersebut sangat dibutuhkan. Penggunaan kembali obat (drug repurposing) merupakan salah satu alternatif dalam pengembangan dan penemuan obat dengan biaya murah serta waktu yang singkat. Tanaman herbal dapat digunakan sebagai obat dengan khasiat yang lebih baik, efek samping yang lebih sedikit, dan lebih murah. Prediksi interaksi obat-target dan penggunaan kembali obat dapat digunakan untuk mengeksplorasi senyawa herbal potensial. Penelitian ini mengatasi kelemahan klasifikasi biner dengan model DSSL-DTI (Deep Semi Supervised Learning-Drug Target Interaction) yang dioptimasi menggunakan Algoritma Genetika. Tujuan penelitian ini adalah mendeteksi kemungkinan adanya hubungan antar label menggunakan pendekatan klasifikasi multilabel dengan model yang dioptimasi. Data yang digunakan penelitian ini antara lain: data protein, data interaksi senyawa-protein, dan data senyawa herbal. Data protein diperoleh dari situs GeneCards yang berisi kumpulan protein yang berasosiasi dengan COVID-19 dan ditemukan pada manusia. Data interaksi senyawa-protein diperoleh dari situs DrugBank dan SuperTarget. Adapun data senyawa herbal diperoleh dari HerbalDB. Hasil penelitian menunjukkan bahwa dengan menggunakan model SAE-DNN-GA yang diusulkan, prediksi senyawa herbal menghasilkan sepuluh senyawa yang berinteraksi dengan dua protein bernilai relevansi tertinggi, yaitu protein INS (7.094) dan ALB (3.178). Hasil ini diharapkan mampu meningkatkan hasil prediksi kandidat senyawa herbal sebagai obat penyakit COVID-19 menjadi lebih akurat.
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.
Pengembangan Model Prediksi Kelulusan Calon Mahasiswa Sarjana pada Sistem Seleksi SNMPTN IPB Muthahari, Wadudi; Wijaya, Sony Hartono; Syafitri, Utami Dyah
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 1 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.1.59-71

Abstract

Since 2019, the SNMPTN selection process at IPB has used web-based selection media and specific algorithms. However, the process has not yet implemented machine learning-based modeling that can provide recommendations on a student's likelihood of being accepted as an IPB student. This study aims to find out what factors influence prospective students passing the IPB SNMPTN pathway and to develop machine learning modeling using Random Forest and Binary Logistic Regression. Four models were built and trained using hyperparameter tuning. The first model uses all features without balancing. The second model uses all features and SMOTE. The third model uses feature selection and SMOTE, and the fourth uses feature selection by Expert Adjustment (EA) and SMOTE. The results show that the models tested using test data with SMOTE data balancing consistently show higher recall values compared to models without data balancing. The third model with Binary Logistic Regression on West Java data and the second model with Binary Logistic Regression on Non-West Java data show the best recall values of 88.93% and 86.91%, respectively. The modeling results also show that the order of college selection, school index category, academic achievements, and program of study choice significantly impact the prediction of applicants’ passing.
Co-Authors Adhi Kusnadi Agus Buono Ahmad Kamal Nasution ali khumaidi, ali Amin Elhan Annisa Annisa Annisa Saraswati Asep Herman Suyanto Askari, M. Aiman Bramdito, Vandam Caesariadi Cece Sumantri Chamdan L Abdulbaaqiy Chrysanti, Rachma Dacholfany, Imanullah Defina Dondin Sajuthi Efendi, Darda Entang Iskandar Farit M. Afendi Farit M. Afendi Fitrianto, Anwar Hardhienata, Medria Kusuma Dewi Hasibuan, Lailan Sahrina Hasrul Bani Asharudin Hayatina, Fina Charisma Huda Shalahudin Darusman Imanullah Dacholfany Imas Sukaesih Sitanggang Indra Jaya Irman Hermadi Jakaria Jakaria Jessy Mainidar Julio Adisantoso Khaswar Syamsu kusnadi, adhi Laras Mutiara Diva Laras Mutiara Diva Lina Herlina Tresnawati M. Aiman Askari Medria Kusuma Dewi Hardhienata Muhammad Asyhar Agmalaro Muthahari, Wadudi Nababan, Adi Pandu Rahmat Nahrowi Nahrowi Nahrowi Nurwegiono, Muhammad Resnawati Restu Misrianti RR. Ella Evrita Hestiandari Sampurno, Global Ilham Saputra, Feby Tri Siregar, Amril Mutoi Sri Dianing Asri Sri Nurdiati Syahid Abdullah Syamsu , Khaswar Taufik Djatna Toto Haryanto Utami Dyah Syafitri Wijaya, Eko Praja Hamid Wisnu Ananta Kusuma Y Aris Purwanto Yandra Arkeman Yandra Arkeman Yani Nurhadryani Yureni Sahril Dedi