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All Journal Indonesian Journal of Electronics and Instrumentation Systems GEMA TEKNOLOGI BERKALA FISIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmu Lingkungan YOUNGSTER PHYSICS JOURNAL Jurnal Sistem Komputer Program Kreativitas Mahasiswa - Karsa Cipta International Journal of Artificial Intelligence Research Jurnal Penelitian Pendidikan IPA (JPPIPA) INDONESIAN JOURNAL OF APPLIED PHYSICS Journal of Physics and Its Applications Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Vokasi Scientific Journal of Informatics
Jatmiko Endro Suseno
Department Of Physics, Faculty Of Science And Mathematics, Diponegoro University, Semarang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Other

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Volume Determination of Symmetrical Object with Distance Parameter Using Linear Regression Method Suseno, Jatmiko Endro; Setyawan, Agus; Gunadi, Isnain
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.906

Abstract

The object’s volume is a consideration in determining the quantity of products such as eggs, fruit, piles of rice, or sand. This research aims to obtain a system for determining the volume of a symmetrical object using the linear regression method in real-time, faster, more effective, and more enjoyable. This research uses segmentation methods and linear regression to determine the volume of a symmetrical object. The objects are a pile of rice and eggs which have symmetrical shapes. The shape of the symmetry in each object is a cone for a pile of rice and an oval for an egg. The results of this research are a system of symmetrical object volume determination using the linear regression method with an accuracy score of 96.48% for piles of rice and 97.84% for an egg. This system has limitations, there are the volume value must be in the data range that has been trained and the camera phone must be the same.
Smart Fish Feeder Dengan Medeteksi Jumlah Getaran Permukaan Air Kolam Berdasarkan Tingkat Kelaparan Ikan Jatmiko Endro Suseno; Agus Setyawan; Thessa Putri Aulia
Jurnal Penelitian Pendidikan IPA Vol 11 No 3 (2025): March
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i3.10342

Abstract

Suboptimal fish feeding management can affect the growth rate of fish. Fish   Feeding management consists of aspects of feed nutritional content, quantity, and feeding system. A system that still relies on humans or manual labor is very likely to cause human error in the process, so utilizing technology can be an option to improve the feeding system. This study aims to design and test the Smart Fish Feeder Design as an innovation in technology-based freshwater fish feeding. The fish Feeder works by providing fish feeding scheduling using the RTC DS3231 and detecting vibrations in pond water, which aims to determine the feeding process using the SW-420 Vibration Sensor. If a certain vibration is used as a parameter that the fish are still consuming feed, then the feeding process continues. The duration of the influence of sensor readings in adding feed lasts for one minute and can be adjusted according to pond conditions or the number of research subjects. The results show that the design can provide fish feed on a scheduled basis with high accuracy and can detect water vibrations as an indicator of feed consumption by fish.
Optimization of Mineral Fuel Export Forecasting Using Attention-based Long Short-Term Memory Prasetya, Ananda; Suseno, Jatmiko Endro; Sutikno
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38381

Abstract

Purpose: This study aims to optimize the forecasting of the Net Value of Indonesia's mineral fuel exports using the Attention-based Long Short-Term Memory (LSTM) model, supported by Dropout and Recurrent Dropout techniques that are combined to produce an optimal model. Methods: Modeling uses an LSTM architecture equipped with an Attention mechanism, as well as Dropout and Recurrent Dropout. The research procedure uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The research material used is the Indonesian mineral fuel export dataset with HS code 27 from 2014 to 2025. Model was built using the Random Search method to optimize hyperparameters such as the number of neurons (units), activation functions (Tanh, ReLu), and optimizers (Adam, Nadam, RMSprop). Result: The Attention-based LSTM model with Dropout and Recurrent Dropout techniques achieved a MAPE of 7.76%, which was better than the other models tested. Attention analysis shows that lag 12 has the greatest dominance, while lags 11 to 10 also contribute significantly, indicating an annual seasonal pattern. Projections for the next 12 months show a moderate decline in Net Value, in line with seasonal trends and historical data. Novelty: The main contribution of this research is the optimization of an Attention-based LSTM model using a combination of Dropout and Recurrent Dropout techniques, which is effective in forecasting Indonesia's mineral fuel export values because it is able to capture annual seasonal patterns, thereby improving the accuracy and stability of the forecast results.
A Real-Time Hooke's Law Experiment using IoT Mobile Application Sudarmanto, Agus; Poernomo, Joko Budi; Suseno, Jatmiko Endro; Putranto, Ari Bawono; Basit, Muhammad Abdul
Journal of Physics and Its Applications Vol 8, No 1 (2026): February 2026
Publisher : Diponegoro University Semarang Indonesia

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

Abstract

Hooke's Law is one of the topics in physics that can be simplified for better understanding through practical methods. This research aims to design a Real-Time remote Hooke's Law experiment in laboratory with Blynk as IoT Mobile Application, allowing students to experiment more flexibly in terms of time and location. The research methodology is based on Research and Development (R&D), including hardware design, software design, testing and data collection, data analysis, and report writing. This study resulted in the development of a laboratory Hooke's Law experiment and a Blynk application as its controller. The apparatus was tested by conducting experiments with three different loads: 30 grams, 40 grams, and 50 grams. The experiments aimed to obtain the spring constant, k, which were then processed using Hooke's Law formula based determine spring elongation data, ∆x. The 30 gram load yielded an average ∆x of 0.059 meters with an accuracy of 99.98% and an average k value of 4.90 N/m with an accuracy of 98.75%. The 40 gram load yielded an average ∆x of 0.059 meters with an accuracy of 99.93% and an average k value of 5.32 N/m with an accuracy of 95.40%. The 50 gram load yielded an average ∆x of 0.089 meters with an accuracy of 99.94% and an average k value of 5.53 N/m with an accuracy of 96.39%. The overall accuracy of the apparatus was 99.95% for ∆x and 96.18% for the spring constant. The system can choose the mass, m with control the stepper motor via mobile application and the result of research can be monitored in smartphone display such as such as spring elongation, ∆x, and spring constant, k as well as streaming video for monitoring purposes. [1]      Arsada, Bakhtiyar, and B. Suprianto, “Ultrasonic Sensor Application for Distance Position Detection in Space Using Arduino Uno” State Univ. of Surabaya, 6(2), 137-145, (2017).[2]      K. A. Gamage, D. I. Wijesuriya, S. Y. Ekanayake, A. E. Rennie, C. G. Lambert, and N. Gunawardhana, "Online delivery of teaching and laboratory practices: Continuity of university programmes during COVID-19 pandemic" Educ. Sci., 10(10), 291, (2020). [3]      H. S. Wattimena, A. Suhandi, and A. Setiawan, “Indonesian Physics Education Journal” Indones. Phys. Educ. J., 10(2), 128–139, (2014).[4]      B. K. Prahani, E. Hariyono, H. V. Saphira, I. Zakhiyah, S. Eliezanatalie, and M. H. Ismail, "Digitalization of Physics Laboratory Tools: Increase Undergraduate Students Learning Motivation and Problem-Solving Skills" TEM J., 14(3), 2371–2380, (2025).[5]      S. Madakam, R. Ramaswamy, and S. Tripathi, "Internet of Things (IoT): A Literature" J. Comput. Commun., 3, 164–173, (2015).[6]      M. N. Ramadhani, "Real Laboratory Praktikum Kefisien Muai Panjang Berbasis Internet of Things dan Aplikasi Android" S1 Thesis, Universitas Islam Negeri Walisongo Semarang, (2021).[7]      Z. Wan, Y. Song, and Z. Cao, "Environment dynamic monitoring and remote control of greenhouse with ESP8266 NodeMCU" in Proc. 2019 IEEE 3rd Inf. Technol., Netw., Electron. Autom. Control Conf. (ITNEC), 377–382, (2019). [8]      D. C. Giancoli, Physics, Vol. 1, Jakarta: Erlangga, (2001).[9]      N. Azman, Internet of Things dan Komputasi Edge: Pengenalan Hingga Keamanan, Jakarta: CV. Tampuniak Mustika Edukarya, (2020).[10]  A. Kusumaningrum, A. Pujiastuti, and M. Zeny, "Pemanfaatan Internet of Things pada Kendali Lampu" Compiler, 6(1), 53–59, (2017). [11]  T. Juwariyah, S. Prayitno, and A. Mardhiyya, "Perancangan Sistem Deteksi Dini Pencegah Kebakaran Rumah Berbasis ESP8266 dan Blynk" J. Transistor EI, 3(2), 120–126, (2018).[12]  I. Setiawan and D. Sutarno, "Pembuktian Eksperimental Pengaruh Jumlah Lilitan Pegas dan Diameter Pegas terhadap Konstanta Pegas" in Conf. Proc. Sci., (2011).[13]  P. F. Yudha and R. A. Sani, "Implementasi sensor ultrasonik HC-SR04 sebagai sensor parkir mobil berbasis Arduino" Einstein E-J., 5(3), 19–26, (2019).[14]  G. N. Prakasa, "Prototipe Kunci Pintu Menggunakan Motor Stepper Berbasis Arduino Mega 2560 Dengan Perintah Suara Pada Android" S1 Thesis, Universitas Lampung, (2017).[15]  Y. Efendi, "Internet of Things (IoT) Light Control System Using Mobile-Based Raspberry Pi" Sci. J. Comput. Sci., 4(1), 19–26, (2018). [16]   C. Dziuban, C. R. Graham, P. D. Moskal, A. Norberg, and N. Sicilia, "Blended learning: the new normal and emerging technologies" Int. J. Educ. Technol. High. Educ., 15(1), 3, (2018).[17]  C. Dziuban, C. R. Graham, P. D. Moskal, A. Norberg, and N. Sicilia, "Blended learning: the new normal and emerging technologies" Int. J. Educ. Technol. High. Educ., 15(1), 3, (2018).[18]  M. R. Hidayat, S. Christiono, and S. S. Budi, “Design of IoT-Based Home Security System with NodeMCU ESP8266 Using PIR HC-SR501 Sensor and Smoke Detector Sensor” Kilat J., 7(2), 140–141, (2018).A. Cocco and S. C. Masin, "The Law of Elasticity" Psicologica, 31(3), 647–657, (2010). 
Performance Comparison of Random Forest, XGBoost, and SVM for Flood Risk Prediction Using BNPB GIS Data Mz, Muhammad Amanulloh; Nurhayati, Oky Dwi; Suseno, Jatmiko Endro
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1461

Abstract

This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—for predicting flood risk using spatial and non-spatial data from BNPB GIS. The analysis focuses on disaster records from January 3 to 15, 2026, with district-city as the spatial unit of observation. Following data cleaning, exploratory analysis, and feature preparation, the models were evaluated using ROC-AUC, PR-AUC, F1-Score, Precision, Recall, and Accuracy. XGBoost demonstrated the highest ROC-AUC (0.675), indicating strong overall performance in distinguishing flood from non-flood events. Random Forest achieved the highest Recall (0.947), showing superior sensitivity in detecting flood events, while SVM exhibited fluctuating performance with a lower ROC-AUC (0.496). Visualizations of model behavior and spatial flood patterns were provided to support model interpretability. The study’s results suggest that ensemble models, particularly XGBoost and Random Forest, can significantly enhance flood risk prediction, improving the accuracy and sensitivity of early warning systems. These findings contribute to the development of more effective data-driven flood mitigation strategies in Indonesia, enabling better disaster preparedness and response.
Co-Authors Achmad Supriyadi, Achmad Agus Setyawan Agus Subagio Agus Sudarmanto Agus Sulistiyo Agus Syafrudin Ainie Khuriati Ali Khumaeni Anak Agung Istri Sri Wiadnyani Andri Wibowo Ari Bawono Putranto Ari Bawono Putranto Arlien Siswanti Asep Yoyo Wardaya Basit, Muhammad Abdul Binu Soesanto, Qidir Maulana Catur Edi Widodo Dian Anggraini Djalal Er Riyanto Evi Setiawati Fatkhur Rohman Figur Humani Fitria L Giga Verian Pratama Glar Donia Deni Habib Sabil Rosyidi Hadi, Muhammad Rafli Irsyad Heri Sugito Hudzaifah Hazazi Zia Kusuma Humairoh Ratu Ayu I Gusti Ngurah Antaryama I Nyoman Sujana Ibnu Arimono Inayatul Inayah Irwan Agus Saputro Isnaeni Isnaeni Isnain Gunadi Isnain Gunadi K. Sofjan Firdausi Karyadi, Kukuh Kasto Wijoyo Teguh Guntoro kusminto , joko budi poernomo, kusminto Kusworo Adi Megarini Hersaputri Moch. Abdul Mukid Much. Azam Muchammad Azam, Muchammad Muhammad Hidayat Muhammad Nur Muhammad Nur Mustafid Mustafid Mz, Muhammad Amanulloh Nurhady Mustofa Oky Dwi Nurhayati Pandji Triadyaksa Prabowo, Muhammad Nur Prasetya, Ananda Priyono Priyono Putra, Hisbicus Dwi Surya Putra, Satrio Sandi Putri, Yurixa Sakhinatul R Rizal Isnanto Ratna Dewi Winesthi Ratu Bilqis Redemtus Heru Tjahjana Reza Lutfi Ismai Rin Hafsahtul Asiah Rinaldo Turang, Rinaldo Ririn Sulpiani S. Suryono Sari, W.T. Satriyo Adhy Sela Ade Otaviana Sudarno Sudarno Sumariyah Sumariyah Suryono Suryono Susilo Hadi Susilo Hadi, Susilo SUTIKNO Thessa Putri Aulia Tomy Kusbramanto Udi Harmoko Widiasmoro, Andi Wiktasari Sari, Wiktasari Windarta, Jaka Yundari, Yundari Yusup Hidayat Yuyu Wahyudin Zaenal Arifin Zaenul Muhlisin Zakiyyah, A.Z.