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All Journal Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Ilmiah Media Sisfo Jurnal Riset Informatika bit-Tech Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Teknologi Informatika dan Komputer Nusantara Science and Technology Proceedings Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Data Sciences Indonesia (DSI) IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Kohesi: Jurnal Sains dan Teknologi Journal of Sustainability, Society, and Eco-Welfare Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Emerging Statistics and Data Science Journal
Trimono
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Shapley Additive Explanations Interpretation of the XGBoost Model in Predicting Air Quality in Jakarta Iffadah, Adhisa Shilfadianis; Trimono; Dwi Arman Prasetya
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1286.5 KB) | DOI: 10.34288/jri.v7i3.366

Abstract

Air quality degradation has become an increasing global problem since 2008, including in Jakarta. By 2024, air pollution in Jakarta is estimated to cause 8,400 deaths and losses of around 34 billion rupiah. To address air pollution, air quality prediction is needed using historical data of Jakarta Air Quality Index from January 2021 to May 2024. The XGBoost ensemble model was chosen for its ability to handle complex data and prevent overfitting. And Shapley Additive Explanations (SHAP) to understand how the model makes decisions. Results showed the XGBoost model achieved MAPE 4.44%. Analysis with Shapley Additive Explanations (SHAP) identified PM2.5 was significantly affected by max and PM10 features, while O3, CO, SO2, and NO2 remained relevant. An increase in PM10 tends to increase PM2.5 concentrations, suggesting the need to control this parameter to improve air quality. These results are important to provide a better understanding of the dynamics of air quality as well as provide a reference for the government in formulating more effective policies or preventive measures in Jakarta.
ANALISIS PERBANDINGAN METODE K-MEDOID DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING PADA DATA KONSUMSI REMPAH-REMPAH DI KABUPATEN / KOTA Rhomaningtias, Lina; Kusharyadi, M. Nurhadyatullah; Westerdam Sean Jatindra, Reagen; Trimono; Nasrudin, Muhammad
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7071

Abstract

Konsumsi rempah-rempah di Indonesia mencerminkan keragaman budaya, geografi, dan pola hidup masyarakat di berbagai daerah. Namun, kajian kuantitatif yang memetakan pola konsumsi rempah antar kabupaten/kota masih terbatas, meskipun data statistik tersedia secara terbuka. Penelitian ini bertujuan untuk mengeksplorasi dan membandingkan efektivitas dua metode klasterisasi K-Medoid dan Agglomerative Hierarchical Clustering (AHC) dalam mengelompokkan wilayah berdasarkan kesamaan konsumsi enam jenis rempah utama: bawang merah, bawang putih, bawang bombay, cabai merah, cabai hijau, dan cabai rawit. Data sekunder berasal dari Badan Pusat Statistik tahun 2024, dengan preprocessing berupa pembersihan data, standarisasi, serta reduksi dimensi menggunakan Principal Component Analysis (PCA). Evaluasi dilakukan menggunakan metrik validasi internal seperti Silhouette Score, Dunn Index, Davies-Bouldin Index, Calinski-Harabasz Index, dan Cophenetic Correlation. Hasil menunjukkan bahwa metode AHC dengan linkage ward dan lima klaster memberikan performa paling optimal dibandingkan K-Medoid. Segmentasi wilayah berdasarkan hasil klaster mengungkapkan struktur konsumsi rempah yang berbeda antara wilayah pedesaan dan perkotaan. Penelitian ini memberikan kontribusi penting dalam pemetaan konsumsi rempah berbasis data dan dapat dijadikan dasar perumusan kebijakan pangan dan pembangunan wilayah yang lebih tepat sasaran.
ANALISIS HUBUNGAN KETERSEDIAAN GURU, RUANG KELAS DAN ANGKA PUTUS SEKOLAH TERHADAP STATUS SEKOLAH MENGGUNAKAN ONE-WAY MANOVA KHAIRUNISA, ADENDA; fernando, Mochamad Firman; rachmanto, Nugroho Fajar; Nasrudin, Muhammad; Trimono
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7188

Abstract

Pendidikan merupakan pilar fundamental dalam pembangunan sumber daya manusia dan kemajuan bangsa. Namun, kesenjangan dalam akses terhadap pendidikan yang berkualitas masih terus terjadi, yang berdampak pada tingginya angka putus sekolah. Studi ini meneliti pengaruh ketersediaan guru, tenaga kependidikan, dan fasilitas ruang kelas terhadap angka putus sekolah, dengan fokus khusus pada perbedaan antara sekolah negeri dan swasta. Dengan menggunakan Analisis Multivariat Satu Arah (MANOVA), penelitian ini menganalisis beberapa variabel dependen secara simultan untuk mengidentifikasi perbedaan signifikan dalam sumber daya pendidikan dan angka putus sekolah. Dataset yang digunakan berasal dari Kementerian Pendidikan Indonesia, mencakup variabel utama seperti jumlah guru, jumlah ruang kelas, dan tingkat putus sekolah. Uji asumsi statistik, termasuk uji Box’s M, Chi-Square Bartlett, dan uji Mardia, dilakukan untuk memvalidasi analisis MANOVA. Hasil penelitian menunjukkan bahwa status sekolah berpengaruh signifikan terhadap distribusi sumber daya pendidikan dan angka putus sekolah siswa. Temuan ini memberikan wawasan berharga bagi para pembuat kebijakan dalam merancang strategi untuk meningkatkan pemerataan pendidikan dan mengurangi angka putus sekolah.
Hyperparameter optimization of XGBoost using artificial bee colony for predicting medical complications in hemodialysis patients Laksana Aryananda, Rangga; Trimono; Syaifullah J, Wahyu; Wan Awang, Wan Suryani
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.459

Abstract

Chronic Kidney Disease (CKD) is a serious global health issue, ranking as the 12th leading cause of death in 2019, with a 31.7% increase since 2010. Many CKD patients require hemodialysis, which poses risks of complications such as hypertension, hypotension, and gastrointestinal disorders, increasing mortality. This study predicts hemodialysis complications using XGBoost optimized with the Artificial Bee Colony (ABC) algorithm. The dataset includes numerical and categorical variables such as blood pressure, hemoglobin levels, gender, and complication history. To improve class distribution, the Synthetic Minority Over-sampling Technique is applied. Five test scenarios with different ABC parameter configurations were conducted to optimize XGBoost hyperparameters. Results indicate that balancing the dataset with SMOTE enhances model accuracy. Among the tested scenarios, Test 3, with ABC parameters n_bees set to 30, max_iter set to 30, and limit set to 10, achieved the highest accuracy, increasing from 89% (unbalanced) to 94% (balanced). Although training time increased, the improved performance highlights the potential of the XGBoost-ABC framework for early complication detection. This approach can enhance patient care, reduce mortality risks, and support clinical decision-making for hemodialysis patients.
Multinomial Logistic Application on Factors Affecting Poor Population in East Java Isyanto, Aisyah Kirana Putri; Trimono; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2400

Abstract

Poverty is still one of the major problems in East Java, even though this province has an important role in supporting the national economy. This condition shows that development in each district/city has not been evenly distributed, so a data-based analysis is needed to determine the factors that influence the poverty rate. This study aims to analyze the influence of socioeconomic variables on the poverty rate category in East Java using a multinomial logistic regression model. The data used is secondary data from the Central Bureau of Statistics (BPS) in 2023 which covers 38 districts/cities. The independent variables analyzed consisted of life expectancy, average years of schooling, open unemployment rate, labor force participation rate, expenditure per capita, human development index, and gross regional domestic product (GRDP) per capita. The analysis process involved data exploration, multicollinearity test, multinomial logistic regression modelling, simultaneous and partial parameter significance test, and model performance evaluation. The results show that per capita expenditure is the only variable that has a significant effect on poverty level classification. The model is able to classify the data with an accuracy of 81% and a McFadden R² value of 0.6483, which means the model has a fairly good performance. This finding shows the importance of increasing people's purchasing power as an effort to reduce poverty. This research is expected to be a reference for local governments in formulating more targeted and data-based policies.            
Food Price Prediction Using the Vector Moving Average (VMA) Model in Surabaya and Malang Najma P., Safira; Trimono; Diyasa, I Gede Susrama Mas
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2428

Abstract

Price fluctuations of animal-based food are a significant issue in Indonesia, especially for low-income communities. In 2023, chicken prices increased by 4.55% and beef prices rose by 11%, contributing to inflation in East Java. Fluctuations in the prices of tuna and milkfish also affected purchasing power and the consumption of animal protein, which remains relatively low compared to other Asian countries. This study aims to predict the prices of animal-based food commodities in Surabaya City and Malang Regency using the Vector Moving Average (VMA) method, which is capable of capturing strong correlations among variables in multivariate time series data. The study covers daily prices per kilogram of beef, chicken, tuna, and milkfish throughout 2023. The 14-day price prediction at the beginning of 2024 shows that the best model for Surabaya is VMA(2), while for Malang Regency, it is VMA(3), selected based on ACF and PACF plots, low AIC values, MAPE values, and consistency of prediction results. The evaluation results using the Mean Absolute Percentage Error (MAPE) indicate that in Surabaya, beef (0.63%), chicken (2.51%), and tuna (4.76%) achieved high prediction accuracy, while milkfish (13.38%) falls into the “good” category. In Malang Regency, the VMA(3) model yielded more consistent prediction results, with all commodities showing MAPE values below 10%: beef (5.62%), chicken (2.43%), tuna (5.61%), and milkfish (2.18%). These results show that the VMA model performs well in capturing the price dynamics of food commodities, as evidenced by the low MAPE values.
Implementasi Model BiLSTM-Attention untuk Prediksi Nilai IHSG Berdasarkan Data Historis OHLCV Ramadhanti, Amirah Rizky; Putri, Safira Rahmalia; Trimono; Mohammad Idhom
Jurnal Ilmiah Media Sisfo Vol 19 No 2 (2025): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2025.19.2.2392

Abstract

The Composite Stock Price Index (IHSG) reflects the performance of the Indonesian capital market, but predicting it is challenging due to high volatility and the influence of various external factors. This study aims to develop and evaluate a deep learning-based predictive model using a Bidirectional Long Short-Term Memory (BiLSTM) architecture combined with an Attention Mechanism to predict the IHSG value based on historical numerical data (OHLCV). This method was chosen for its ability to recognize bidirectional sequential patterns and highlight the most relevant historical information in the prediction process. The research was conducted quantitatively using an experimental approach, and model evaluation was performed using regression metrics such as R², RMSE, MAE, and MAPE. The results obtained showed excellent predictive performance with an R² of 0.9485, MAPE of 0.63%, RMSE of 59.47, and MAE of 45.12. Additionally, attention weight analysis revealed that the model focuses more on the last two days within the prediction time window, indicating that recent information significantly influences IHSG movements. These findings suggest that the BiLSTM-Attention approach is effective in capturing stock market dynamics and has the potential to serve as a strategic tool for data-driven investment decision-making.
Analisis sentimen program makan bergizi gratis menggunakan bidirectional gated recurrent unit Krisnawan; Zufar Abdullah Rabbani; Trimono; Mohammad Idhom
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 3 (2025): IT-Explore Oktober 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i3.2025.pp282-294

Abstract

The Free Nutritious Meals (MBG) program launched by the Indonesian government aims to address the problem of malnutrition in children and students. However, the acceptance of this program in the community still requires in-depth evaluation because there are many negative sentiments that dominate on social media. This study aims to analyze the sentiment of the Indonesian community regarding the Free Nutritious Meals program on social media X (Twitter) using the Bidirectional Gated Recurrent Unit (BiGRU) model. Of the 1,405 tweet data obtained, 57% were negative opinions and 43% were positive opinions. The evaluation results show that the BiGRU model with FastText support to handle potential overfitting, is able to classify sentiment effectively, with an accuracy of 80%. Sentiment analysis shows that the majority of public responses to the Free Nutritious Meals (MBG) program tend to be negative, with 798 negative tweets and 607 positive. This reflects public dissatisfaction with the implementation of the program and highlights the need for evaluation and improvements so that the benefits can be more widely felt by the community.
PERBANDINGAN FUNGSI AKTIVASI GAUSSIAN DAN MULTIKUADRATIK PADA RADIAL BASIS FUNCTION NEURAL NETWORK UNTUK PREDIKSI INDEKS HARGA KONSUMEN DI SURABAYA Fiqih Pavita Andharluana; Aviolla Terza Damaliana; Trimono
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.56751

Abstract

Indeks Harga Konsumen (IHK) merupakan indikator penting dalam mengukur tingkat inflasi yang digunakan sebagai dasar pengambilan kebijakan ekonomi, termasuk penyesuaian gaji, upah, dan kontrak kerja. Karena IHK memiliki pengaruh penting terhadap perubahan laju inflasi perekonomian Indonesia, maka perlu dilakukan prediksi terhadap IHK untuk membantu pemerintah dalam merumuskan kebijakan yang tepat, baik dalam stabilisasi harga maupun perlindungan terhadap kesejahteraan masyarakat terutama di wilayah dengan aktivitas ekonomi tinggi seperti Kota Surabaya, yang memiliki pertumbuhan Produk Domestik Regional Bruto (PDRB) signifikan. Penelitian ini bertujuan untuk membandingkan kinerja dua fungsi aktivasi dalam model Radial Basis Function Neural Network (RBFNN), yaitu Gaussian dan Multiquadratik, dalam memprediksi laju IHK di Surabaya. Metode RBFNN dipilih karena kemampuannya dalam menangkap pola non-linear pada data deret waktu. Metodologi penelitian meliputi pengumpulan data dari Badan Pusat Statistik (BPS), pra-pemrosesan data, pengembangan model, dan evaluasi menggunakan data uji. Model RBFNN dibangun dengan menentukan kluster, nilai spread, fungsi aktivasi, dan output, serta dievaluasi menggunakan Symmetric Mean Absolute Percentage Error (sMAPE). Data yang digunakan berupa deret waktu Indeks Harga Konsumen (IHK) Kota Surabaya periode Januari 2006 hingga Desember 2024 dengan frekuensi bulanan, sehingga diperoleh 228 data observasi. Berdasarkan hasil analisis, diperoleh bahwa fungsi aktivasi Gaussian memberikan hasil prediksi terbaik dengan nilai SMAPE sebesar 0.70%, yang menunjukkan tingkat akurasi sangat tinggi. Hasil prediksi IHK untuk bulan Januari hingga Mei 2025 berturut-turut adalah 107.61, 108.09, 108.54, 108.95, dan 108.32.
Penerapan Repeated Measures MANOVA One-i pada Analisis Data Pendidikan Dasar di Indonesia: Application of Repeated Measures MANOVA One-i in Data Analysis of Elementary Education in Indonesia Gestyaki, Jacinda Ardina; Hadin, Tiara Audrey Anugerah; Anggie, Erna Novita; Nasrudin, Muhammad; Trimono
Jurnal Kolaboratif Sains Vol. 8 No. 11: November 2025
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v8i11.8823

Abstract

Penelitian ini menerapkan Repeated Measures Multivariate Analysis of Variance (RM MANOVA) One-Way sebagai metode statistik multivariat untuk menganalisis perbedaan indikator pendidikan dasar di Indonesia berdasarkan status sekolah negeri dan swasta. Tiga variabel dependen yang ditinjau adalah jumlah siswa, siswa yang mengulang, dan siswa yang putus sekolah, dengan data bersumber dari Portal Data Pendidikan Dasar dan Menengah tahun 2023. Sebelum analisis utama, dilakukan serangkaian uji asumsi guna memastikan kelayakan model, meliputi Bartlett’s Test untuk menilai kesamaan varians, Box’s M Test untuk menguji homogenitas matriks kovarians, serta Mardia’s Skewness–Kurtosis untuk memverifikasi normalitas multivariat. Hasil analisis RM MANOVA menunjukkan adanya perbedaan signifikan antara sekolah negeri dan swasta pada ketiga variabel dependen, dengan nilai Wilks’ Lambda = 0,6655 dan Pillai’s Trace = 0,3345 (p < 0,001). Uji lanjutan menggunakan ANOVA Univariat memperlihatkan pengaruh signifikan status sekolah terhadap jumlah siswa mengulang (F = 36,47; p < 0,001) dan jumlah siswa putus sekolah (F = 20,69; p < 0,001). Selanjutnya, uji Post-Hoc Tukey mengonfirmasi adanya perbedaan rata-rata yang nyata pada kedua variabel tersebut. Temuan ini menunjukkan bahwa RM MANOVA lebih unggul dibandingkan pendekatan univariat karena mampu menangkap keterkaitan antar variabel secara simultan, sehingga memberikan pemahaman yang lebih menyeluruh terhadap data yang kompleks. Oleh karena itu, penelitian ini berkontribusi tidak hanya dalam menjelaskan perbedaan capaian pendidikan dasar, tetapi juga dalam menegaskan relevansi penggunaan RM MANOVA sebagai pendekatan statistik yang efektif pada analisis data multivariat di bidang sosial.
Co-Authors Abda Abda Afidria, Zulfa Febi Ajeng Puspa Wardani Aji Riyantoko, Prismahardi Alfan Rizaldy Pratama Alzam , Muhammad Arsyad Amri Muhaimin Ananta, Aditya Putra Anggie, Erna Novita Anugrah, Muhammad Cahya Raka Ardiani, Ardia Eva Ardra Jamie Hibatullah Ardra Jamie Hibatullah, Genesis Arfiansyah, Muhammad Nabil Putra Arifta, Septia Dini Aryaputra Jagaddatri Auralia, Karina Aviolla Terza Damaliana Baktiar Putri, Milla Akbarany Bhalqis, Anissa Andiar Cokro, Risbuwono Heru Damaliana, Aviolla Terza Dewi, Ni Luh Ayu Nariswari Diana Novitasari, Diana Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Farhan Syah Putra Wiyono Fatmala, Friza Nur fernando, Mochamad Firman Fiqih Pavita Andharluana Gestyaki, Jacinda Ardina Hadin, Tiara Audrey Anugerah Hayu, Nahda Hibatulah, Ardra Hidayah, Amellia Harmaimun I Maruddani , Di Asih idhom, Mohammad Iffadah, Adhisa Shilfadianis Irawan, Tanaya Anindita Isyanto, Aisyah Kirana Putri Junior, Nouval Arya Kaffi, Laisal Kamila, Rosyidatul Karnaen, Amelia Zafira Kartika Maulida Hindrayani Khairunisa, Adenda Krisnawan Kristanaya, Mirechelin Kusharyadi, M. Nurhadyatullah Kusharyadi, Muhammad Nurhadyatullah Laksana Aryananda, Rangga Maulana, Mohammad Hikmal Maulidya Prastita Syah Melinda Putri Azzahra Mohammad Idhom Muhaimin, Amri Muhammad Azkiya Akmal Muhammad Nasrudin Najma P., Safira Namira, Alivia Salma Nurdiana, Pinka Ozzari, Nikita Aprilia Pakpahan, Vera Febrianti Pasha, Naufal Ricko Maulana Puti Cresti Ekacitta Putri, Safira Rahmalia rachmanto, Nugroho Fajar Ramadani, Nurmalita Ramadhanti, Amirah Rizky Rhomaningtias, Lina Rizkiyah, Selly Rizqin, Indira Zein Sakhi, Difta Alzena Selayanti, Nabilah Sinulingga, Kevin Brema Saputra Susrama Masdiyasa, I Gede Syaifullah J, Wahyu Syamsiar, Syamsiar Wan Awang, Wan Suryani Westerdam Sean Jatindra, Reagen Witanto, Steffany Marcellia Zufar Abdullah Rabbani