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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Jurnal Manajemen Teknologi Jurnal Agro Ekonomi JAM : Jurnal Aplikasi Manajemen Indonesian Journal of Business and Entrepreneurship (IJBE) JUITA : Jurnal Informatika Indonesian Journal of Biotechnology Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Manajemen. AL ISHLAH Jurnal Pendidikan International Research Journal of Business Studies (E-Journal) Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ekonomi Integra JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications JURNAL AGRONIDA Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya ComTech: Computer, Mathematics and Engineering Applications Jurnal Manajemen Inferensi Jurnal Agro Ekonomi International Journal of Advances in Data and Information Systems Journal of Data Science and Its Applications Jurnal Teknik Informatika (JUTIF) JURNAL ILMIAH GLOBAL EDUCATION Xplore: Journal of Statistics STATISTIKA Asian Journal of Social and Humanities Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) International Research Journal of Business Studies Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications
I Made Sumertajaya
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor Kampus Darmaga, Bogor 16680
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Scientific Journal of Informatics

 1 
Biclustering-Based Analysis to Identify Fruit Production Potential in Indonesia Using Plaid Model Algorithm Alwani, Nadira Nisa; Sumertajaya, I Made; Wigena, Aji Hamim
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.25054

Abstract

Purpose: The application of biclustering using the plaid model aims to simultaneously identify mapping or grouping patterns of provinces and fruit type in Indonesia. The performance evaluation of the plaid model algorithm is used to assess its capability to discover and generate optimal biclusters, thereby representing the relationship between regions and fruit types with similar production characteristics. Methods: The plaid model algorithm produces optimal biclusters by configuring parameter scenarios such as model selection, managing the number of layers, and determining threshold values for rows and columns. The Average Mean Square Residue (MSR) value and the number of biclusters that can provide the most relevant data are used to determine the optimal parameter selection. Result: The plaid model algorithm effectively grouped provinces and fruit varieties into multiple biclusters. The row-constant model was choosen based on the average MSR value of 2.0537, which formed five overlapping biclusters across provinces and fruit types. Several provinces, such as Central Java and West Java, demonstrated a high potential for rose apples, breadfruit, and salak. Other provinces showed comparatively moderate levels of production. Novelty: This study presents a novel way to apply the plaid model biclustering algorithm to data on fruit varieties in various Indonesian provinces. Rarely used in horticulture, this method offers an alternative perspective on structured commodity mapping, especially when identifying specific patterns between fruit varieties and geographic distribution.
A Hybrid Sampling Approach for Handling Data Imbalance in Ensemble Learning Algorithms Astari, Reka Agustia; Sumertajaya, I Made; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.19163

Abstract

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection. Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP). Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean. Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.
Rice Price Forecasting for All Provinces in Indonesia Using The Time Series Clustering Approach and Ensemble Empirical Mode Decomposition Ilmani, Erdanisa Aghnia; Sumertajaya, I Made; Fitrianto, Anwar
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.23536

Abstract

Purpose: Accurate forecasting of rice prices is essential to ensure food security and a healthy economy for a country like Indonesia. Problems regarding time-series phenomena, such as trends or seasonality, are problematic for traditional approaches like ARIMA (Autoregressive Integrated Moving Average). This study analyzes the effect of EEMD (Ensemble Empirical Mode Decomposition) combined with time-series data clustering on forecasting accuracy. Methods: From 2009 until 2023, the thirty-two Indonesian provincial rice prices were grouped monthly into time-series clusters using hierarchical clustering, average linkage, and DTW (Dynamic Time Warping). After clusterization, the time series were decomposed using the ensemble EEMD method to extract their IMFs (Intrinsic Mode Functions) and residual components. Each IMF was assigned an ARIMA model. The model forecast was generated by adding all individual estimates. MAPE (Mean Absolute Percentage Error) was used to measure the model's performance. Result: The prices were divided into three clusters with an optimized region. Price changes are well captured through EEMD, where the residual components contributed predominantly to the long-term trends. The validation of the prediction showed MAPE values under 10% for the majority of the provinces, which indicates a relatively accurate prediction. On the other hand, some regions had inaccuracies that were higher than others due to uncontrollable fluctuations. Novelty: This study integrates clustering with EEMD decomposition for monthly rice price forecasting using data from 32 Indonesian provinces from 2009 - 2023, offering a novel approach that improves traditional techniques. The model can capture distinct regional price patterns and provide essential information to policymakers to manage rice supply and price stabilization. Further studies can develop external hybrid models with economic variables.
Co-Authors A Kurnia A. A. Mattjik AA Mattjik Abd. Rasyid Syamsuri Abdu Alifah Abdul Aziz Nurussadad Ade Gusalinda Adelia Putri Pangestika Agus Mohamad Soleh Agustin Faradila Ahmad Anshori Mattjik Ahmad Ansori Matjjik Ahmad Ansori Mattjik Ahmad Ansori Mattjik Aidi, Muhammad N Aini, Febri Nur Aji Hamim Wigena Akbar Rizki Alfian Futuhul Hadi Alwani, Nadira Nisa Amanda Permata Dewi Anang Kurnia Andi Setiawan Andrew Donda Munthe Anggraini Sukmawati Anik Djuraidah Arina, Faula Aropah, Vina Da'watul Aropah, Vina Da’watul ASEP SAEFUDDIN Astari, Reka Agustia Azagi, Ilham Alifa Azis, Irfani Bagus Sartono Budi Susetyo Budi Susetyo Choirun Nisa Chrisinta, Debora Cici Suhaeni Cynthia Wulandari Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Dian Kusumaningrum Dian Kusumaningrum Diki Akhwan Mulya Doni Suhartono Dwi Agustin Nuriani Sirodj Dwi Yulianti Embay Rohaeti Emeylia Safitri Erfiani Erfiani Erfiani Erfiani, Erfiani Erwina Erwina Evita Choiriyah Fadilah, Anggita Rizky FAHREZAL ZUBEDI Fahriya, Andina Faqih Udin dan Jono M. Munandar Meivita Amelia Farit M Afendi Farit Mochamad Afendi Fitria Hasanah Fitrianto, Anwar Gusti Tasya Meilania Halimatus Sa'diyah Hari Wijayanto Haryastuti, Rizqi Hengki Muradi Hidayat, Agus Sofian Eka Hilda Zaikarina Huda, Usep Firdaus I Gede Nyoman Mindra Jaya Ilma Nabila Ilmani, Erdanisa Aghnia Imam Adiyana Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iqbal, Teuku Achmad Irfani Azis Irfani Azis Ismah, Ismah Isti Rochayati Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Jasiulewicz, Anna Khairil Anwar Notodiputro Kurnia, A Kusdaniyama, Nunung Kusman Sadik Laradea Marifni Lestari P, Merryanty Linda Sakinah M. Syamsul Maarif Ma'mun Sarma Manuel Leonard Sirait Manuel Leonard Sirait Manuel Leonard Sirait Mattjik, AA Maulida, Annisaturrahmah Mega Pradita Pangestika Meilania, Gusti Tasya Merryanty Lestari P Mintarto Mundandar, Jono Muhamad Nur Aidi Muhammad Amirullah Yusuf Albasia Muhammad N Aidi Muhammad Nur Aidi Muhammad Ulinnuha Mulianto Raharjo Munanda, Jono Mintarto Muradi, Hengki Newton Newton Nina Valentika Ningsih, Wiwik Andriyani Lestari Noercahyo, Unggul Sentanu Novi Hidayat Pusponegoro Nunung Kusdaniyama Nunung Kusdaniyama Nur Hikmah Nurlia Eka Damayanti Nurus Sabani Pasaribu, Sahat M. Pepi Novianti Pika Silvianti Pratiwi, Windy Ayu Pratiwi, Windy Ayu Pudji Muljono Purwaningsih, Siti Samsiyah Puspasari, Novia Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rhesa Adisty, Mohamad Risnawati, I'lmisukma Rizqi Haryastuti Sahat M. Pasaribu Sarah Fadhlia Sarma, Ma’mun Satria Yudha Herawan SATRIYAS ILYAS Setyono Setyono Setyono Sirait, Manuel Leonard Siti Samsiyah Purwaningsih Sri Surjani Tjahjawati Sunardi Sunardi Sunardi Suruddin, Adzkar Adlu Hasyr Sutomo, Valantino A Syafitri, Utami Syella Sumampouw Tsabitah, Dhiya Tsabitah, Dhiya Ulayya Ulfah Sulistyowati Utami Dyah Syafitri Valantino A Sutomo Valentika, Nina Wibowo, Dwi Yoga Ari Winda Nurpadilah Windi D.Y Putri Wiwik Andriyani Lestari Ningsih Yenni Angraini Yoga, Ibnu Abi Zulkarnain, Rizky