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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Al Khidma: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Natural Mosquito Repellet Socialization for Dengue Prevention in Rural Areas: Sosialisasi Semprotan Anti Nyamuk Alami untuk Pencegahan Demama Berrdarah M Zufar Irhab S Putra; Sekar Arum Melati; Dinda Putri Arnindi; Desy Miftachul Ilmi Arifin Putri; Trimono
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 3 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dengue fever remains a significant public health concern, necessitating comprehensive prevention strategies, including vector control. This community engagement study explores the implementation of natural mosquito repellent spray production using lemongrass as a strategic approach to mitigate dengue transmission in Lontar Village. The research aims to analyze the impact of this natural repellent on community health, identifying challenges and opportunities associated with its adoption and sustainable production. Employing a mixed-methods approach, the study incorporates community surveys, interviews with local residents and health officials, and direct observation during the socialization and production phases. Findings indicate that the provision of natural mosquito repellent significantly enhances community participation in dengue prevention, offering an accessible and environmentally friendly alternative to chemical repellents. Key challenges include initial awareness dissemination regarding the benefits of natural repllents and ensuring consistent home-based production. However, the benefits demonstrate substantial potential for improving local health outcomes, fostering self-reliance in disease prevention, and promoting the use of local resources. The study suggests targeted support mechanisms, including continuous education, hands-on workshops, and simplified production guidelines to maximize the benefits of this natural repellent. These findings contribute to understanding the role of community-based interventions in local health development and provide practical insights for supporting disease prevention efforts in rural contexts. The research highlights the importance of strategic interventions that can empower communities to adopt sustainable health practices.
ARIMA-TGARCH Model for Return Prediction and Risk Estimation with VaR Imanta Ginting; Trimono Trimono; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3090

Abstract

Investment activity in the Indonesian capital market has experienced significant growth, driven by increasing public awareness and accessibility to financial instruments. Stocks remain the most favored investment tool due to their potential for high returns, though they come with higher risks. Accurate modeling of return dynamics and risk estimation is thus crucial for informed investment decisions. This study analyzes the return and volatility of PT Telekomunikasi Indonesia Tbk (TLKM) stock using a hybrid time series approach that combines the Autoregressive Integrated Moving Average (ARIMA) model and the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) model. The analysis uses daily closing price data from 2020 to 2024, with 1,210 observations. The best-fitting model, ARIMA(2,0,2)–TGARCH(1,1), resulted in low Root Mean Squared Error (RMSE) values of 0.0188 for both training and testing datasets, indicating strong prediction accuracy. Forecasting over a five-day horizon revealed fluctuating returns and a decreasing trend in volatility, from 0.0230 to 0.0198. Additionally, the study utilized the Value at Risk (VaR) method to estimate potential losses under normal market conditions. At a 95% confidence level, the predicted daily loss for a capital investment of IDR 50,000,000 ranged between IDR 1,633,108 and IDR 1,859,355. The combination of ARIMA and TGARCH, integrated with VaR, provides a comprehensive framework for capturing both linear return trends and asymmetric volatility, offering investors a robust quantitative tool for managing risks and optimizing strategies.
KLASTERISASI TINGKAT KESEJAHTERAAN MASYARAKAT MENGGUNAKAN METODE SELF ORAGNIZING MAPS DENGAN PARTICLE SWARM OPTIMIZATION Afidria, Zulfa Febi; Trimono, Trimono; Prasetya, Dwi Arman
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7752

Abstract

Pulau Jawa juga merupakan salah satu pulau yang masih menjadi kontributor terbesar dalam pertumbuhan ekonomi Indonesia. Kontribusi Pulau Jawa diperkirakan akan menyentuh porsi hingga 58,75 persen pada tahun 2023. Namun, di balik pertumbuhan ekonominya yang pesat, Pulau Jawa masih menghadapi tantangan kesejahteraan seperti tingginya pengangguran, kemiskinan, serta rendahnya kualitas sumber daya manusia dan pendidikan. penelitian ini bertujuan untuk mengelompokkan tingkat kesejahteraan Masyarakat di Pulau Jawa menggunakan metode Self Organizing Maps dengan Particle Swarm Optimization. Metode ini dipilih karena SOM juga sangat efisien dalam mengelola data yang mengandung noise, outlier, serta nilai yang hilang karena ukuran sampelnya tidak memiliki batasan. Akan tetapi SOM juga memiliki kelemahan yaitu jumlah cluster perlu ditentukan secara spesifik dan untuk mendapatkan batas cluster peneliti harus melakukan inspeksi manual atau menggunakan algoritma cluster hierarki atau partisi. Penentuan batas cluster pada metode SOM dapat menggunakan metode Particle Swarm Optimization(PSO). Kebaruan dari penelitian ini adalah penerapan kombinasi metode SOM dan PSO dalam analisis kesejahteraan masyarakat di Pulau Jawa, yang masih jarang digunakan pada studi serupa. hasil penelitian ini menunjukkan model terbaik membentuk 3 cluster dengan nilai silhouette coefficient tertinggi sebesar 0.7293 Nilai tersebut menunjukkan bahwa struktur cluster yang terbentuk termasuk dalam kategori baik.
Implementasi Extremely Randomized Trees dengan Optimasi Hyperparameter Accelerated Particle Swarm Optimization untuk Klasifikasi Subtipe Anemia Adelia, Adelia; Trimono, Trimono; Idhom, Mohammad
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11295

Abstract

Anemia is a health problem that negatively affects both medical outcomes and social well-being, highlighting the need for accurate early detection. This study applies a machine learning approach to classify anemia subtypes to support clinical intervention and further examination. The Extra Trees method employs a hierarchical decision-tree structure with extreme randomization, making it robust to overfitting and capable of good generalization on small to medium datasets. Accelerated Particle Swarm Optimization (APSO) is utilized as an efficient optimization technique to improve classification performance. The novelty of this study lies in integrating Extra Trees with APSO to optimize anemia subtype classification. The dataset consists of 385 records collected from a regional hospital in East Java, Indonesia, covering four classes: thalassemia, iron deficiency anemia, anemia of chronic disease, and non-anemia. The features include patient initials, gender, age, and hematological parameters (Hb, HCT, RBC, MCV, MCH, MCHC, RDW). The optimized model achieved 85% accuracy, 87% precision, 85% recall, 85% F1-score, 95% specificity, and 94% AUC, outperforming the non-optimized model. These results indicate that the proposed approach is effective for anemia subtype classification.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Nova Auliyatul Maulidiyyah; Trimono Trimono; Aviolla Terza Damaliana; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
BUDIKDAMBER: Inovasi Budidaya Lele dan Sayuran dalam Ember Untuk Masyarakat RW 04 Kelurahan Sambikerep, Kota Surabaya Nevia Desinta Putri; Arrum Marwani; Mochammad Abudrrochman Faiz; Bagus Widduro; Trimono Trimono
Al Khidma: Jurnal Pengabdian Masyarakat Al Khidma Vol. 6 No. 1 Januari - Maret 2026
Publisher : Sekolah Tinggi Ilmu Al-Qur'an (STIQ) Amuntai Kalimantan Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35931/ak.v6i1.5799

Abstract

Budikdamber (Budidaya Ikan dan Sayuran dalam Ember) merupakan inovasi budidaya akuaponik sederhana yang memadukan budidaya ikan lele dan sayuran dalam satu wadah ember di wilayah RW 04 Kelurahan Sambikerep, Kota Surabaya. Program ini bertujuan meningkatkan ketahanan pangan rumah tangga melalui pemanfaatan lahan terbatas secara efisien, sekaligus mendukung upaya penurunan stunting dengan menyediakan sumber protein hewani dan sayuran segar. Pelaksanaan kegiatan dilakukan dengan pendekatan partisipatif melalui pelatihan dan pendampingan oleh mahasiswa Universitas Pembangunan Nasional Veteran Jawa Timur kepada masyarakat setempat. Hasilnya menunjukkan peningkatan pengetahuan dan keterampilan warga dalam budidaya ikan lele dan tanaman kangkung secara mandiri, dengan antusiasme tinggi. Budikdamber terbukti sebagai solusi praktis dan berkelanjutan untuk meningkatkan konsumsi gizi keluarga di lingkungan perkotaan. Disarankan adanya dukungan lanjutan dari pemerintah lokal dan perluasan sosialisasi agar program dapat direplikasi ke wilayah lain. Inovasi ini memiliki potensi besar dalam mendukung ketahanan pangan serta pemberdayaan masyarakat di kawasan padat penduduk.
Spatial Modeling of Highly Dispersed Poverty Data in East Java Using the Geographically Weighted Negative Binomial Regression Method Sugiarti, Nova Putri Dwi; Trimono, Trimono; Wara, Shindi Shella May
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2636

Abstract

Poverty in East Java Province is a complex issue characterized by diverse regional characteristics. This study aims to model the number of poor inhabitants in East Java while accounting for overdispersion and spatial heterogeneity using the Geographically Weighted Negative Binomial Regression (GWNBR) method with four kernel weighting functions. The results indicate that the Negative Binomial regression model performs well at the global level, with an AIC of 941.01. However, to capture local variation in poverty drivers, the GWNBR model with an Adaptive Bisquare kernel proved optimal, yielding the lowest AIC of 309.32. These findings confirm the diversity of predictor variable influences across regencies and cities, as evidenced by the significance of variables in Blitar Regency. The GWNBR approach provides more accurate local parameter estimates than the global model, serving as a strategic tool for the government to design more targeted and effective poverty alleviation policies for each region.
PENGARUH PERUBAHAN TAHUN TERHADAP PRODUKSI PERTANIAN DI INDONESIA MENGGUNAKAN PENDEKATAN REPEATED MEASURES MANOVA Selly Rizkiyah; Indira Zein Rizqin; Milla Akbarany Baktiar Putri; Muhammad Nasrudin; Trimono Trimono
RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i1.14970

Abstract

This study aims to analyze significant differences in rice paddy production in Indonesia based on year factors using the Repeated Measures MANOVA method. The data used includes harvest areas, productivity, and total rice production from various provinces during the period 2020-2024.  The results showed that there was a significant relationship between the variables tested, so the independence assumption in the MANOVA method was not met. Therefore, Repeated Measures-MANOVA was used as an alternative approach that is more suitable for repeated data. The analysis showed that there were significant differences in rice production by year, with a p-value of <0.05 in all multivariate statistics. The results highlight the importance of efficient crop land management and increased productivity to support the sustainability of the agricultural sector. The Repeated Measures-MANOVA approach proved effective in identifying variations in production based on time factors and can be a relevant analytical tool.
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Cindi Adam; Mohammad Idhom; Trimono Trimono
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/zwvk1v20

Abstract

The development of artificial intelligence (AI) has driven innovation in financial analysis, including the prediction of volatile stock prices. This study aims to predict the stock price of PT Garudafood Putra Putri Jaya Tbk using an ARIMA model with Outlier handling as an initial approach towards a more adaptive prediction system. Daily closing price data from Yahoo Finance was analyzed through stationarity testing, ARIMA model identification, log-return-based Outlier detection, and performance evaluation using RMSE, MAE, and MAPE. The results show that ARIMA Outlier performs better than the basic ARIMA. The standard ARIMA produces a MAPE of 1.32% and an AIC of –899.46, while ARIMA with three dummy Outliers achieves a MAPE of 1.16% and an AIC of –900.37. The 14-day forecast shows a stable pattern in the range of Rp 370–371. In the test data, the basic ARIMA provided the best accuracy in mid-August, while ARIMA Outlier achieved the highest accuracy at the end of August with a prediction of Rp 370.2, which was very close to the actual price of Rp 370.4. These results show that handling Outliers improves the accuracy of the model, so that ARIMA Outlier can be used as a starting point for the development of an AI-based financial prediction system.
Stacked LSTM Integrated with Big Data Pipelines for Automated Food Beverage Stock Price Prediction Ilil Musyarof Asfiani; Dwi Arman Prasetya; Trimono Trimono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3687

Abstract

Stock price volatility in the Food and Beverage (F&B) sector presents persistent challenges for investors and decision-makers, particularly in emerging markets. This study proposes an automated stock price prediction framework whose primary contribution lies in the system-level integration of a Stacked Long Short-Term Memory (LSTM) model with a scalable big data orchestration pipeline, rather than in introducing a new forecasting algorithm alone. The system targets three Indonesian F&B companies PT Indofood CBP Sukses Makmur Tbk, PT Mayora Indah Tbk, and PT Garudafood Putra Putri Jaya Tbk using historical daily stock price data. The dataset spans multiple years of trading records retrieved from the Yahoo Finance API, and predictions are generated for a seven-day forecasting horizon. Methodologically, the approach combines a multi-layer LSTM architecture with Apache Spark for distributed data preprocessing, Apache Airflow for automated workflow orchestration, and PostgreSQL for structured data storage. This integration enables scheduled data ingestion, reproducible model training, and continuous forecasting within an end-to-end analytics pipeline. Model performance is evaluated using error-based metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), and is benchmarked against a conventional single-layer LSTM without pipeline orchestration. Empirical results show that the proposed pipeline-based Stacked LSTM achieves lower prediction error, with MAPE values ranging between approximately 1.1% and 2.2% across the evaluated stocks, indicating improved stability and accuracy. Overall, the findings demonstrate enhanced forecasting reliability and deployment readiness through automated pipelines.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Adelia Adelia, Adelia Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Arrum Marwani Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bagus Widduro Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Cindi Adam Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eny Widayawati Erna Novita Anggie Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Ilil Musyarof Asfiani Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Jacinda Ardina Gestyaki Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Mas&#039;ad Mas&#039;ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mochammad Abudrrochman Faiz Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Nevia Desinta Putri Ningrum, Imelda Widya Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putri, Irma Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Sekar Arum Melati Selly Rizkiyah Shindi Shella May Wara Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tiara Audrey Anugerah Hadin Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wahyu Syaifullah Jauharis Saputra Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widison, Daffin Tanjiro Yuciana Wilandari Zalfa Assyadida, Azizah