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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 Warta Penelitian Perhubungan 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

Author-ID : 417442

Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Transportation Veterinary Other

Published : 29 Documents Claim Missing Document

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Ekspansi Kueri pada Sistem Temu Kembali Informasi Berbahasa Indonesia Menggunakan Analisis Konteks Lokal Diva, Laras Mutiara; Wijaya, Sony Hartono
Jurnal Ilmu Komputer dan Agri-Informatika Vol 1, No 1 (2012)
Publisher : Departemen Ilmu Komputer IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (151.024 KB)

Pengguna suatu sistem temu kembali sering kali tidak tepat mengungkapkan kebutuhan informasi yang diinginkannya dalam bentuk kueri. Masalah lain ialah adanya perbedaan pilihan kata antara seorang pengguna dalam kuerinya dan penulis dalam dokumennya. Analisis konteks lokal adalah ekspansi kueri otomatis yang mengombinasikan teknik global dan teknik lokal. Analisis konteks lokal mengurutkan konsep berdasarkan pada kemunculannya dengan seluruh term kueri pada dokumen peringkat teratas dan menggunakan konsep peringkat teratas untuk ekspansi kueri. Pada dasarnya suatu dokumen mempunyai beberapa topik sehingga pada penelitian ini dokumen peringkat teratas dibagi ke dalam beberapa passage. Konsep peringkat teratas diambil dari beberapa passage peringkat teratas. Tujuan penelitian ini ialah mengimplementasikan ekspansi kueri menggunakan analisis konteks lokal. Kinerja dari sistem temu kembali informasi menggunakan analisis konteks lokal bagus dengan nilai ketepatan rata-rata sebesar 60%. Hasil penelitian menunjukkan bahwa kinerja sistem dengan analisis konteks lokal secara signifikan meningkat 6.07% dibandingkan dengan sistem tanpa analisis konteks lokal dengan dokumen-dokumen relevan yang ditemukembalikan berada pada posisi teratas penemukembalian. Selain itu, jumlah dokumen dan passage peringkat teratas yang terambil secara signifikan tidak mempengaruhi nilai ketepatan rata-rata. Faktor yang lebih mempengaruhi adalah jumlah term ekspansi yang ditambahkan. Analisis konteks lokal cukup baik diterapkan pada koleksi dokumen yang memiliki kemiripan cukup tinggi.Kata kunci: analisis konteks lokal, ekspansi kueri

Ekspansi Kueri pada Sistem Temu Kembali Informasi Berbahasa Indonesia Menggunakan Analisis Konteks Lokal Laras Mutiara Diva; Sony Hartono Wijaya
Jurnal Ilmu Komputer dan Agri-Informatika Vol 1 No 1 (2012)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Determination of Morphological Characteristics in Kuantan Cattle using Multivariate Analysis Restu Misrianti; Jessy Mainidar; Hasrul Bani Asharudin; Yureni Sahril Dedi; Arsyadi Ali; Sony Hartono Wijaya; Cece Sumantri; Jakaria Jakaria
Buletin Peternakan Vol 45, No 3 (2021): BULETIN PETERNAKAN VOL. 45 (3) AUGUST 2021
Publisher : Faculty of Animal Science, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21059/buletinpeternak.v45i3.66868

The objective of this research was to characterized morphology and estimated genetic distance between intra population of Kuantan cattle. A Total of 213 cattle (44 male and 169 female with age ranging from 2-3 years) were used in this study and collected from extensive ranging systems in Three sub-population (Cerenti, Inuman, and Kuantan Hilir regions) Kuantan Singingi Regency, Riau Province. Five variables were measured that is Body Length (BL)(cm), Wither Height (WH)(cm), Hip Height (HH)(cm), Chest Girth (CG)(cm), and Chest Depth (CD)(cm). Data obtained were descriptive analysis, Principal Components Analysis (PCA) and Hierarchichal Clustering Analysis (HCA) using XLSTAT program. All variables of body measurement in the Kuantan Hilir region were higher than Cerenti dan Inuman, Kuantan Singingi Regency. The first factor in PCA described body measurement contributed 32.77%, and the second factor described body shape contribute 25.83% of total variability. The dendrogram showed there is Three clusters of Kuantan Cattle based on this research.

Development of Knowledge Management System of Teachers’ Competency Annisa Saraswati; Irman Hermadi; Sony Hartono Wijaya
Jurnal Pendidikan Teknologi dan Kejuruan Vol 25, No 1 (2019): (May)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Teachers had a strategic role in helping achieving an educational purpose. Every teacher should have specific competencies. Sustainable quality improvement of teachers needed to get support and attention from various stakeholders including leaders at schools and government. Therefore, a medium was required to facilitate the process of various knowledge of teacher competency which had been documented and unlimited to time and location. One of the solutions was developing a knowledge management system. The research method was adopted from Knowledge Management System Life Cycle (KMSLC) method. Results of this research was a web-based knowledge management system with features supporting the process of capturing, developing, distributing, and utilizing knowledge. Development of the knowledge management system could facilitate teachers to gain knowledge related to teachers’ competencies easily.

Asosiasi Single Nucleotide Polymorphism pada Diabetes Mellitus Tipe 2 Menggunakan Random Forest Regression Lina Herlina Tresnawati; Wisnu Ananta Kusuma; Sony Hartono Wijaya; Lailan Sahrina Hasibuan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1046.657 KB)

Precision medicine can be developed by determining association between genomic data, represented by Single Nucleotide Polymorphism (SNP), and phenotype of diabetes mellitus type 2 (T2D). The number of SNP is actually very abundance. Thus, sorting and filtering the SNP is required before conducting association. The purpose of this paper was to associate SNP with T2D phenotypes. SNP ranking was conducted to choose significant SNPs by calculating importance score. Selected SNPs were associated with T2D phenotype using random forest regression. Moreover, the epistasis was also examined to show the interactions among SNPs affecting phenotype. This paper obtained 301 importance SNPs. Top ten SNPs have association with five T2D protein candidates. The evaluation results of the proposed models showed the Mean Absolute Error (MAE) of 0.062. This results indicate the success of random forest regression in conducting SNP and phenotype association and epistatic examination between two SNPs.

Pemodelan Berbasis Jaringan untuk Pengklasifikasian Kanker Payudara Berdasarkan Data Molekuler Mushthofa; Chamdan L Abdulbaaqiy; Sony Hartono Wijaya; Muhammad Asyhar Agmalaro; Lailan Sahrina Hasibuan
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.101-113

Cancer is a disease characterized by uncontrolled cell growth. One of the characteristics of uncontrolled growth is the presence of estrogen-receptor-positive (ER+). About 67% of breast cancer test results have ER+. Breast cancer profiles are divided into 4 subtypes, namely: Luminal A, Luminal B, basal-like, and HER-2 enriched. Each category has a different effect on adjuvant chemotherapy. In this study, a network-based approach was used to select features/molecular biomarkers that have the potential to assist modeling and classifying sub-types of breast cancer. The molecular features used are Copy Number Alteration (CNA) and gene expression. The feature selection results were compared with the PAM50 feature-based accuracy from the literature study. The results indicate that the features selected from this network-based approach can obtain a comparable performance w.r.t the original PAM50 features, and can be used as alternative to perform breast cancer subtyping.

Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning Syahid Abdullah; Wisnu Ananta Kusuma; Sony Hartono Wijaya
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13984

Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.

Comparison of Dairy Cow on Morphological Image Segmentation Model with Support Vector Machine Classification Amril Mutoi Siregar; Y Aris Purwanto; Sony Hartono Wijaya; Nahrowi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Pattern recognition is viral in object recognition and classification, as it can cope with the complexity of problems related to the object of the image. For example, the category of dairy cows is essential for farmers to distinguish the quality of dairy cows for motherhood. The current problem with breeders is still using the selection process manually. If the selection process using the morphology of dairy cows requires the presence of computer vision. The purpose of this study is to make it easier for dairy farmers to choose the mothers to be farmed. This work uses several processes ranging from preprocessing, segmentation, and classification of images. This study used the classification of three segmentation algorithms, namely Canny, Mask Region-Based Convolutional Neural Networks (R-CNN), and K-Means. This method aims to compare the results of the segmentation algorithm model with SVM); the model is measured with accuracy, precision, recall, and F1 Score. The expected results get the most optimal model by using multiple resistant segmentation. The most optimal model testing achieved 90.29% accuracy, 92.49% precision, 89.39% recall, and 89.95% F1 Score with a training and testing ratio of 90:10. So the most optimal segmentation method uses the K-Means algorithm with a test ratio of 90:10.

The effect of segmentation on the performance of machine learning methods on the morphological classification of Friesien Holstein dairy cows Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p59-68

Many classification algorithms are in the form of image pattern recognition; the approach to the complexity of the problem should be a feature of feasibility for representing images. The morphology of dairy cows greatly affects their health and milk production. The paper will apply several classification methods based on the morphology of Holstein Friesian dairy cows. To improve the accuracy of the model used, the segmentation process is the right step. In this paper, we compare several machine learning algorithms to get optimal accuracy. The algorithm used a support vector machine (SVM), artificial neural networks, random forests and logistic regression. Segmentation methods used are mask region-based convolutional neural network (R-CNN) and Canny; optimal accuracy is expected to create intelligent applications. The success of the method is measured with accuracy, precision, recall, and F1 Score, as well as testing by conducting a training-testing ratio of 90:10 and 80:20. This study discovered an artificial neural network optimal model with Canny with an accuracy of 82.50%, precision of 87.00%, recall of 79.00%, F1-score of 81.62%, and testing ratio of 90:10. While the models with the highest 80:20 ratio achieved 84.39% accuracy, 88.46% precision, 80.47%, and 83.00% F1-score with mask R-CNN with logistic regression.

Analisis Sentimen Pengguna Twitter terhadap Vaksinasi COVID-19 di Indonesia menggunakan Algoritme Random Forest dan BERT Amin Elhan; Medria Kusuma Dewi Hardhienata; Yeni Herdiyeni; Sony Hartono Wijaya; Julio Adisantoso
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 2 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.2.199-211

The Covid-19 pandemic has encouraged many stakeholders to be able to adapt to current conditions. One of the programs launched by the government in order to overcome the spread of Covid-19 is to run a vaccination program. In order to find out the public's interest in the Covid-19 vaccination program that was launched, it is necessary to carry out a sentiment analysis. Sentiment analysis is generally done to obtain the latest information from a large corpus. The purpose of this study is to analyze the sentiments of Twitter users towards the Covid-19 vaccination in Indonesia using the Random Forest and BERT Algorithms. The research stages include pre-processing Twitter data related to Covid-19 vaccination topics, sentiment labeling, handling unbalanced data, classifying datasets using the Random Forest and BERT algorithms, as well as analysis and evaluation. After handling unbalanced data, the results of Twitter user sentiment analysis for Covid-19 vaccination in Indonesia yielded an accuracy of 81%, F1-score of 74%, precision of 76%, and recall of 74% using the Random Forest algorithm and an accuracy of 82%, F1-score 79%, precision of 78%, and recall of 79% using the BERT Algorithm. Although the BERT Algorithm has generally a slightly higher performance than the Random Forest Algorithm, the simulation results show that the Random Forest algorithm has significantly lower computation time compared to the BERT algorithm in the considered case.

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

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