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All Journal International Journal of Electrical and Computer Engineering Buletin Peternakan JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Veteriner 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 Jurnal INFOTEL 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

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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.

Refinement of methodology and deep computational analysis of the thermal images for better estimates of pregnancy diagnosis in cynomolgus monkeys (Macaca fascicularis) Huda Shalahudin Darusman; Sony Hartono Wijaya; Ahmad Kamal Nasution; Entang Iskandar; Dondin Sajuthi
Jurnal Veteriner Vol 22 No 4 (2021)
Publisher : Faculty of Veterinary Medicine, Udayana University and Published in collaboration with the Indonesia Veterinarian Association

The current use of thermal imaging has been documented in wild animals due to the benefit for having real-time results with less or almost no restrain or invasive methods required - and this is significant for better well-being. This paper will explore the thermal imaging studies as a part of employing non-invasive methods in evaluating physiological function, in particular with refinement of the methods, followed by further computational analysis of the images to ensure the validity of the methods as predictive tools for pregnancy diagnosis. We conducted refinements in thermal imaging methods and computational analysis of deep learning for pregnancy diagnosis of cynomolgus monkeys (Macaca fascicularis) at breeding facility of The Primate Research Center, LPPM IPB University. Subjects were already identified by ultrasound as pregnant in 80, 120 and 130 days. Thermal images along with the temperature data were obtained from FLIR ONE camera in sedated animals with dorso-ventral recumbence. The temperature data were analyzed with linear regression to correlate the skin temperature and the days of pregnancy to make a prediction of pregnancy days based on temperature data. There is a positive correlation of the temperature to the pregnancy days with a function of temperature to days. Further computational analysis of the thermal image, the results showed that the refined methods and the computational analysis brought better interpretation to evaluate health and reproductive status, in particular with the pregnancy diagnosis.

Effects of spectral transformations in support vector machine on predicting 'Arumanis' mango ripeness using near-infrared spectroscopy Ali Khumaidi; Y. Aris Purwanto; Heru Sukoco; Sony Hartono Wijaya
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.856.206-215

One of the challenges of exporting Arumanis mangoes is their accurate grading ability because the mangoes do not change color during ripening. Near-Infrared (NIR) spectroscopy is a non-destructive method for detecting the internal ripeness of fruit which is quite reliable. However, NIR absorbance bands are often nonspecific, extensive, and overlapping. Although SVM modeling is quite good in performance, it can still be improved by spectral transformation. In this study, 11 spectral transformation operations were compared with their combinations to find the best input model. Spectral transformation operations include SAVGOL, RNV, BASELINE, MSC, EMSC, NORML, CLIP, RESAMPLE, DETREND, SNV, and LSNV. In the 2 class classification model, the highest accuracy is obtained using RNV and SAVGOL. The prediction model for SSC content with the best MSE value uses 3 combinations of spectral transformation operations, namely DETREND, LSNV, and SAVGOL with parameter values: 'deriv_order': 0, 'filter_win': 31, 'poly_order': 6. As for the prediction model of mango hardness with The best MSE value uses 2 combinations of spectral transformation operations, namely LSNV and SAVGOL with parameter values: deriv_order ': 0,' filter_win ': 15,' poly_order ': 6.

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.

Co-Authors Adhi Kusnadi Agus Buono Ahmad Kamal Nasution Ali Khumaidi Amin Elhan Annisa Annisa Annisa Saraswati Aris Purwanto 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 Heru Sukoco 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 Puput Irfansyah 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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