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Trimono
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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.
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.
Prediksi Penyaluran Obat Kandungan Misoprostol dengan Metode Temporal Convolutional Networks Ramadani, Nurmalita; Idhom, Mohammad; Trimono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Aborsi ilegal di Indonesia masih menjadi permasalahan serius, terutama dengan maraknya penggunaan misoprostol yang diperjualbelikan secara ilegal. Indonesia mencatat sekitar 1,7 juta kasus aborsi per tahun, dengan 42,5 dari setiap 1.000 wanita usia subur di Pulau Jawa terlibat dalam praktik ini. Berdasarkan laporan kasus, penyalahgunaan misoprostol dapat menyebabkan komplikasi serius seperti hipertermia, hipoksia, hingga kematian akibat kegagalan multiorgan. Selain itu, ditemukan bahwa 73% obat aborsi yang dijual online mengandung misoprostol, dan lebih dari 300.000 situs penjual obat ilegal telah diblokir oleh Kementerian Komunikasi dan Informasi. Salah satu celah yang mempermudah penyalahgunaan adalah belum adanya regulasi batas kuantitas penyaluran obat tersebut. Penelitian ini menerapkan model Temporal Convolutional Networks (TCN) untuk memprediksi pola penyaluran obat misoprostol menggunakan data primer dari BPOM dengan periode 2021-2024. Hasil evaluasi menunjukkan bahwa TCN secara konsisten lebih unggul dibandingkan LSTM pada semua panjang input. TCN mencatat rata-rata penurunan NMAE sebesar 85% dan NMSE sebesar 68% dibandingkan LSTM. Pendekatan berbasis TCN ini diharapkan dapat membantu otoritas dalam meningkatkan pengawasan distribusi obat serta mendukung kebijakan pengendalian misoprostol agar tidak disalahgunakan.   Abstract Illegal abortion in Indonesia remains a serious problem, especially with the widespread use of misoprostol, which is sold illegally. Indonesia records around 1.7 million abortion cases per year, with 42.5 out of every 1,000 women of childbearing age on the island of Java involved in this practice. According to case reports, the misuse of misoprostol can lead to serious complications such as hyperthermia, hypoxia, and even death due to multi-organ failure. Additionally, it was found that 73% of abortion drugs sold online contain misoprostol, and over 300,000 illegal drug-selling websites have been blocked by the Ministry of Communication and Information. One loophole that facilitates misuse is the lack of regulations on the quantity of the drug's distribution. This study applied the Temporal Convolutional Networks (TCN) model to predict the distribution patterns of misoprostol using primary data from the Indonesian Food and Drug Administration (BPOM) for the period 2021-2024. Evaluation results show that TCN consistently outperforms LSTM across all input lengths. TCN achieves an average reduction of 85% in NMAE and 68% in NMSE compared to LSTM. This TCN-based approach is expected to assist authorities in enhancing drug distribution oversight and supporting misoprostol control policies to prevent misuse.
CIRCLE: A digital platform for circular food waste management in achieving sustainable food security Auralia, Karina; Dewi, Ni Luh Ayu Nariswari; Witanto, Steffany Marcellia; Trimono
Journal of Sustainability, Society, and Eco-Welfare Vol. 3 No. 2: January (2026)
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jssew.v3i2.2026.2436

Abstract

Background: Food loss and waste (FLW) pose a major global challenge, threatening food security, economic stability, and environmental sustainability. In Indonesia, despite abundant resources, inefficiencies in production and distribution still lead to significant waste and nutritional inequality. Overcoming this issue requires an integrated and sustainable system that improves redistribution efficiency. Supported by digital innovations such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, the circular economy approach offers a strategic solution. In response, the CIRCLE platform was developed as a smart and sustainable digital system for food redistribution. Methods: This study uses a descriptive method through a literature review to identify theories, concepts, and best practices on circular economy, based digital platforms for reducing FLW. Secondary data from scientific publications and institutional reports were analyzed to form the conceptual basis for designing the CIRCLE (Circular Utilization of Food Resources) platform. Findings: The literature emphasizes the importance of multi-stakeholder collaboration and the application of AI, IoT, and data analytics to develop efficient and sustainable food distribution systems. The implementation of user-centered design and gamification is also recommended to enhance user engagement and awareness. Conclusion: The CIRCLE platform represents an innovative and sustainable digital solution to reduce food waste, strengthen food security, and foster collaboration toward achieving SDG 2 and SDG 12 in Indonesia. Novelty/Originality of this article: This study introduces the CIRCLE platform as a distinctive integration of circular economy principles and digital technologies, including AI, IoT, and gamification, within a unified system for reducing food loss and waste in Indonesia.
Implementation of Temporal Fusion Transformer (TFT) for Short-Term Sales Prediction of Telkomsel Data Packages in East Java Muhammad Azkiya Akmal; Trimono; Alfan Rizaldy Pratama
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3268

Abstract

The development of the cellular telecommunications industry has driven an increasing demand for fast, stable, and affordable data services. Accurate forecasting of data package sales is a significant challenge for telecommunications operators due to high demand fluctuations and the complexity of time series patterns. This study aims to implement a Temporal Fusion Transformer (TFT) model based on Seasonal-Trend Decomposition using Loess (STL) to predict short-term sales of Telkomsel data packages in East Java. The data used are sales transactions with hourly time resolution from January to June 2024, focusing on the five data packages with the highest transaction volume. The STL method is applied in the pre-processing stage to separate the trend, seasonal, and residual components, which are then used as additional features in the TFT modeling. Model performance is evaluated using Mean Absolute Error (MAE) and Quantile Risk (q-Risk). The results show that the TFT model is able to produce accurate predictions with an MAE value of 3.6941 and an average q-Risk of 0.0808. Furthermore, interpretability analysis revealed that historical sales variables, seasonal components, and calendar variables significantly contributed to the prediction results. These findings indicate that the STL-based TFT approach is effective for short-term sales forecasting and has the potential to support data-driven operational decision-making in the telecommunications sector.
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 Dewi, Ni Luh Ayu Nariswari Diana Novitasari, Diana 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 Irawan, Tanaya Anindita 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 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