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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

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

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.

Pemilihan Pola Distribusi Pupuk Bersubsidi Pusri ke Gudang Lini III dengan ANP Dacholfany, Imanullah; Wijaya, Sony Hartono; Efendi, Darda
Warta Penelitian Perhubungan Vol. 35 No. 1 (2023): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v35i1.2266

Selaku produsen pupuk urea bersubsidi, PT. Pupuk Sriwijaya Palembang menyalurkan pupuk Urea bersubsidi dari Pabrik yang ada di Kota Palembang ke salah satu wilayah pelayanannya yaitu Provinsi Lampung. Pola distribusi yang ada saat ini sangat dinamis dengan menerapkan 3 pola secara bersamaan yaitu pendistribusian pupuk dalam kantong dengan truk langsung, Port to Door Service (PTDS) dalam kantong (inbag) dan PTDS curah (to Inbag). Selain menentukan pola distribusi yang sesuai dengan kebutuhan perusahaan, Adanya amanat Pemerintah atas kecukupan stok pupuk bersubsidi untuk petani serta audit terhadap kewajaran biaya distribusi yang dikeluarkan menjadi salah satu pertimbangan untuk mengetahui pola distribusi yang tepat untuk dijalankan. Untuk itu dilakukan pemilihan pola distribusi dengan menggunakan kriteria 5 tepat yaitu tepat kualitas, tepat kuantitas, tepat lokasi, tepat biaya dan tepat waktu beserta sub kritera yang ada didalamnya dengan alternatif yang terbentuk sebanyak 7 (tujuh) alternatif yaitu Trucking berupa pupuk dalam kantong (A), PTDS pupuk dalam kantong/Inbag (B), PTDS pupuk curah/to inbag (C), kombinasi A dan B, Kombinasi A dan C, Kombinasi B dan C dan terakhir kombinasi A, B dan C. Hasil analisis Analytic Network Process (ANP) dengan menggunakan aplikasi Super Decision 3.2.0 menunjukan bahwa pendistribusian pupuk dengan trucking terpilih sebagai prioritas pertama dengan nilai rata-rata sebesar 0,231. Hasil perhitungan tersebut disepakati oleh pakar yang berasal dari kelompok praktisi, akademisi dan regulator dengan nilai Kendall’s coefficient (W) sebesar 0,32. Dengan demikian, Trucking dapat dipertimbangkan sebagai pola distribusi yang dapat menjawab kebutuhan PT. Pusri dalam mendistribusikan pupuk urea bersubsidi ke gudang lini III yang ada di Kabupaten/Kota di Provinsi Lampung.

Particle Size Detection of Palm Kernel Cake from Sieving Based on Images Using Convolutional Neural Network Irfansyah, Puput; Purwanto, Yohanes Aris; Wijaya, Sony Hartono; Nahrowi, Nahrowi
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1246

Palm kernel cake (PKC), a by-product of the palm oil industry, is widely used in animal feed due to its economic value. Its utilization reduces the reliance on costly conventional feed ingredients, reducing production expenses and improving livestock efficiency. However, contamination with palm kernel shells remains a key challenge, as it reduces quality and nutritional value. Identifying PKC particle sizes and addressing inconsistencies caused by contamination is complex, requiring advanced computational solutions. This study focuses on classifying the PKC particle sizes -fine, medium and coarse - using image processing combined with machine learning. A sieve shaker is applied to separate particles by size distribution, and a classification model is developed with Convolutional Neural Networks (CNN) under a transfer learning framework, which is effective for limited datasets. Six CNN architectures, MobileNet, Xception, InceptionV3, ResNet-152, VGG16, and NasNetMobile, are tested in four-layer configurations to identify the optimal setup. The results show that the proposed approach can classify PKC particle sizes with high accuracy. Among the models tested, MobileNet provides the best performance, achieving 0.99 accuracy and 0.98 F1 score in the second variation experiment. These findings present a practical and cost-effective method for assessing the quality of PKC, supporting scalable applications in feed production. This approach not only improves the accuracy of the evaluation, but also contributes to efficiency and sustainability in the livestock industry.

Pengembangan Sistem Manajemen Pengetahuan di Organisasi Asosiasi Alumni Program Beasiswa Amerika - Indonesia (ALPHA-I) Nurwegiono, Muhammad; Nurdiati, Sri; Wijaya, Sony Hartono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020712249

Organisasi ALPHA-I (Asosiasi Alumni Program Beasiswa Amerika – Indonesia) memiliki anggota lebih dari 400 orang yang tersebar di sepuluh daerah di Indonesia. Jumlah alumni penerima beasiswa pendidikan dari United States Agency for International Development (USAID) akan bertambah setiap tahun dan akan tergabung di organisasi ini. Hasil observasi menunjukkan bahwa organisasi ALPHA-I memiliki dua masalah utama. Permasalahan pertama adalah ALPHA-I belum menyediakan sarana berbagi pengetahuan tacit pada lima fokus bidang beasiswa USAID. Permasalahan kedua adalah pengetahuan explicit karyawan seperti Standar Operasional Prosedur (SOP), laporan kegiatan, laporan hasil rapat, daftar mitra dan dokumen penting lainnya yang masih dibukukan. Permasalahan tersebut dapat diselesaikan dengan membuat sistem manajemen pengetahuan. Tujuan penelitian ini adalah mengembangkan sistem manajemen pengetahuan yang dapat memudahkan proses menangkap, mengembangkan, membagikan, dan memanfaatkan pengetahuan tacit alumni dan pengetahuan explicit karyawan di organisasi ini. Penelitian ini dilakukan dengan menggunakan metode Knowledge Management System Life Cycle (KMSLC). Hasil dari penelitian ini adalah sistem manajemen pengetahuan yang dibangun dengan framework PHP dan MySQL sebagai Relational Database Management System (RDBMS) berbasis website. Hasil pengujian Black box dari 36 kasus uji yang telah dilakukan menyatakan bahwa semua fungsi pada sistem berjalan sesuai dengan perintah yang diberikan. AbstractThe ALPHA-I Organization (Alumni Association of US - Indonesia Scholarship Programs) has more than 400 members that have spread in ten regions (chapters) in Indonesia. The number of alumni who receive educational scholarships from United States Agency for International Development (USAID) will increase every year and will join this organization. The result of observation to ALPHA-I organization showed that there are two main problems. The first problem is ALPHA-I organization did not provide equipment for the alumni to share their tacit knowledge on five focused areas of USAID scholarships. The second problem is the explicit knowledge of employees to record the Standard Operational Procedure (SOP), activity reports, meeting report, partner list, and other relevant documents were written by books. These problems can be solved by creating a knowledge management system. The purpose of this study is to develop a knowledge management system that can facilitate the process of creation, development, share, and utilize tacit knowledge of alumni and explicit knowledge of employees at ALPHA-I. This research was conducted using the Knowledge Management System Life Cycle (KMSLC) method. The result of this study was a knowledge management system that was built with PHP framework and MySQL-as a Relational Database Management System (RDBMS) based on website. The result of black box testing from 36 case studies demonstrated that all functions in the system run according to the commands given.

Analisis Sentimen Bahasa Indonesia pada Twitter Menggunakan Struktur Tree Berbasis Leksikon Saputra, Feby Tri; Nurhadryani, Yani; Wijaya, Sony Hartono; Defina, Defina
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814133

Jumlah opini di media sosial seperti Twitter tersebar luas sehingga tidak mungkin membaca semua opini untuk mendapatkan seluruh sentimen. Analisis sentimen merupakan salah satu metode untuk mengatasi masalah tersebut. Salah satu pendekatan dalam analisis sentimen adalah berbasis leksikon. Pendekatan berbasis leksikon dapat menghasilkan performa yang baik pada lintas topik pembicaraan tanpa memerlukan pelatihan data. Namun, pendekatan berbasis leksikon sangat bergantung pada kelengkapan dan keragaman sentimen leksikon. Selain itu, hubungan antarkata sangat penting untuk diperhatikan karena dapat mengubah polaritas sentimen pada teks. Hubungan antarkata dapat direpresentasikan dengan baik menggunakan struktur tree. Penelitian ini menggunakan struktur tree sebagai interpretasi hubungan antarkata dalam pembentukan kalimat dengan menambahan kata ke dalam sentimen leksikon. Metode berbasis tree diujikan pada data dengan lintas topik seperti data twit Pilgub Jabar 2018, Pilpres 2019, dan pandemik COVID-19. Ketiga data uji memiliki proporsi kelas yang tidak seimbang, dengan kelas terbanyak merupakan kelas positif. Metode berbasis tree menghasilkan akurasi sebesar 64,97% (meningkat 1,26%) pada data Pilgub Jabar 2018, 64,33% (meningkat 11,41%) pada data Pilpres 2019, dan 66,24% (meningkat 7,61%) pada data pandemik COVID-19. Metode berbasis tree dapat menghasilkan akurasi yang stabil pada beberapa lintas topik dibuktikan dengan standar deviasi akurasi yang kecil (0,97%) bahkan lebih kecil dari metode tanpa tree (5,4%). Metode berbasis tree dapat meningkatkan weighted f1-measure pada data Pilpres 2019 sebesar 10,45% dan data pandemik COVID-19 sebesar 8,1%, sedangkan hasil pada data Pilgub 2018 tidak berbeda secara signifikan. Hasil akurasi dan weighted f1-measure memiliki selisih yang kecil sehingga pengukuran akurasi valid dan tidak bias terhadap data tidak seimbang. AbstractThe number of opinions on social media like Twitter is so widespread that it's impossible to read all those opinions to get all the sentiments. Sentiment analysis is one of the methods that could overcome this problem. The lexicon-based approach is one of the sentiment analysis approaches which perform well across data topics without training. However, the lexicon-based approach relies heavily on the completeness and diversity of sentiment lexicons. The relationship between words is important because it could change the sentiment polarity in the text. The tree structure could represent the relationship between words well. This study uses a tree structure as an interpretation of the relationship between words in a sentence. The tree structure is constructed by adding words to the lexicon sentiment. The tree-based method is tested on cross-topic data such as the tweet data of the 2018 West Java Governor Election, the 2019 Presidential Election, and the COVID-19 pandemic. All data used has an unbalanced class proportion, with the positive class being dominant. The accuracy results of the tree-based method on all data consecutively are 64.97% (increased by 1.26%), 64.33% (increased by 11.41%), and 66.24% (increased by 7.61%). The tree-based method produce stable accuracy on several topics proved by the small accuracies standard deviation (0.97%) that even smaller than the non-tree method (5.4%). The weighted f1-measure increases of the tree-based method on all data consecutively are 0% (equal), 10.45%, and 8.1%. The small difference between the weighted f1-measure and accuracy concludes that the accuracy resulted is valid.

Sistem Rekomendasi Dua Arah untuk Pemilihan Dosen Pembimbing Menggunakan Data Histori dan Skyline View Queries Sampurno, Global Ilham; Annisa, Annisa; Wijaya, Sony Hartono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 5: Oktober 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022955458

Pemilihan dosen pembimbing merupakan salah satu faktor yang mempengaruhi proses penyelesaian tugas akhir. Pada mekanisme pemilihan dosen pembimbing, sering kali mahasiswa sendiri belum memahami dengan jelas kemampuan dirinya serta topik apa yang akan dipilihnya, sehingga nama calon dosen pembimbing yang diusulkan mahasiswa umumnya belum mempertimbangkan hal tersebut. Mekanisme seperti ini juga menyebabkan terjadinya penumpukan calon bimbingan pada dosen tertentu dan kekurangan bimbingan pada dosen yang lain, meskipun keduanya memiliki latar belakang keilmuan yang mirip. Pada saat yang sama, umumnya dosen pembimbing tidak pernah ditanya preferensinya terhadap mahasiswa seperti apa yang sesuai untuk topik penelitian yang akan ditawarkan. Sistem rekomendasi yang ada biasanya hanya mempertimbangkan preferensi salah satu pihak saja, dari sisi dosen saja ataupun sisi mahasiswa saja. Penelitian ini membangun sistem rekomendasi dua arah baik dari sisi dosen maupun dari sisi mahasiswa menggunakan skyline view queries. Skyline view queries merekomendasikan dosen yang dominan kepada mahasiswa sesuai dengan preferensi mahasiswa, dan merekomendasikan mahasiswa yang dominan kepada dosen sesuai dengan preferensi dosen. Untuk mendapatkan preferensi dari kedua sisi, digunakan teknik text mining dan clustering pada data histori nilai akademik dan topik penelitian dari mahasiswa yang sudah lulus sebagai acuan untuk mahasiswa yang akan memilih dosen pembimbing. Hasil percobaan menunjukkan bahwa penggabungan metode skyline view queries dengan profil akademik dan data histori dapat mengatasi permasalahan penumpukan calon bimbingan pada dosen tertentu serta dapat memberikan rekomendasi yang sesuai dengan kemampuan akademik dan preferensi mahasiswa dan dosen. AbstractSelection of thesis supervisor is a factor that have an effect on the final thesis process. In the process of choosing thesis supervisor, student often has not clearly recognize his/her capability and topic that will be researched. Therefore, this issue is likely not considered when the student propose his/her thesis supervisor. This selection process typically also makes one supervisor is proposed by many student while other supervisor is proposed by less student, even though both supervisor has similar scientific background. At the same time, generally the thesis supervisor has never been asked his/her student preferences related to the supervisor’s research topics. Existing recommendation systems usually consider preferences from one party, either supervisor’s or student’s preferences. This research develop a two-way recommendation system, considering both supervisor’s and student’s preferences using skyline view queries. Skyline view queries recommend dominant supervisor to student based on student’s preferences, and recommend dominant student to supervisor based on supervisor’s preferences. To acquire preferences from both party, text mining techniques and clustering is used on student’s historical academic scores data and data of research topics from graduated student as reference for student in choosing thesis supervisor. Experiment results show that using skyline view queries method on student’s academic profile and historical data can overcome the issue of one supervisor is proposed by too many students. In addition, the results shows that the method can also give appropriate recommendation based on student’s academic portfolio and student’s and supervisor’s preferences.

Pemilihan Pola Distribusi Pupuk Bersubsidi Pusri ke Gudang Lini III dengan ANP Dacholfany, Imanullah; Wijaya, Sony Hartono; Efendi, Darda
Warta Penelitian Perhubungan Vol. 35 No. 1 (2023): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v35i1.2266

Selaku produsen pupuk urea bersubsidi, PT. Pupuk Sriwijaya Palembang menyalurkan pupuk Urea bersubsidi dari Pabrik yang ada di Kota Palembang ke salah satu wilayah pelayanannya yaitu Provinsi Lampung. Pola distribusi yang ada saat ini sangat dinamis dengan menerapkan 3 pola secara bersamaan yaitu pendistribusian pupuk dalam kantong dengan truk langsung, Port to Door Service (PTDS) dalam kantong (inbag) dan PTDS curah (to Inbag). Selain menentukan pola distribusi yang sesuai dengan kebutuhan perusahaan, Adanya amanat Pemerintah atas kecukupan stok pupuk bersubsidi untuk petani serta audit terhadap kewajaran biaya distribusi yang dikeluarkan menjadi salah satu pertimbangan untuk mengetahui pola distribusi yang tepat untuk dijalankan. Untuk itu dilakukan pemilihan pola distribusi dengan menggunakan kriteria 5 tepat yaitu tepat kualitas, tepat kuantitas, tepat lokasi, tepat biaya dan tepat waktu beserta sub kritera yang ada didalamnya dengan alternatif yang terbentuk sebanyak 7 (tujuh) alternatif yaitu Trucking berupa pupuk dalam kantong (A), PTDS pupuk dalam kantong/Inbag (B), PTDS pupuk curah/to inbag (C), kombinasi A dan B, Kombinasi A dan C, Kombinasi B dan C dan terakhir kombinasi A, B dan C. Hasil analisis Analytic Network Process (ANP) dengan menggunakan aplikasi Super Decision 3.2.0 menunjukan bahwa pendistribusian pupuk dengan trucking terpilih sebagai prioritas pertama dengan nilai rata-rata sebesar 0,231. Hasil perhitungan tersebut disepakati oleh pakar yang berasal dari kelompok praktisi, akademisi dan regulator dengan nilai Kendall’s coefficient (W) sebesar 0,32. Dengan demikian, Trucking dapat dipertimbangkan sebagai pola distribusi yang dapat menjawab kebutuhan PT. Pusri dalam mendistribusikan pupuk urea bersubsidi ke gudang lini III yang ada di Kabupaten/Kota di Provinsi Lampung.

Designing Halal Product Traceability System using UML and Integration of Blockchain with ERP Kusnadi, Adhi; Arkeman, Yandra; Syamsu , Khaswar; Wijaya, Sony Hartono
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3045

Consuming halal food is mandatory for Muslims, but meeting the growing demand for halal products has been a challenge for Muslim producers. Importing halal products from non-Muslim countries can raise doubts about their halal status. Therefore, a traceability system is needed to ensure the halalness of products. This research proposes a new traceability system by utilizing ERP, Blockchain, and smart contract technologies based on HAS 23000. This study is the first to combine these technologies. Using the System Development Life Cycle (SDLC) method, the design diagram has been successfully developed into an application system prototype. The use of ERP can help companies reduce operational costs, while the combination with blockchain technology ensures more transparent information, data protection, and system security. The system also uses smart contracts to make automated decisions. By managing the procurement of halal products, companies can ensure that products with halal assurance reach consumers.

Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework Kusnadi, Adhi; Arkeman, Yandra; Syamsu, Khaswar; Wijaya, Sony Hartono
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

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.

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