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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) SemanTIK : Teknik Informasi Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Techne : Jurnal Ilmiah Elektroteknika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Terapan (J-TIT) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak Respati Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Computer Science and Information Technology (CoSciTech) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Mohammad Diqi
Universitas Respati Yogyakarta
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : International Journal of Informatics Engineering and Computing

 1 
Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Wandani, Aulia Fadillah Wani
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.103

Abstract

This work examines how effectively the ConcaveLSTM model can forecast sunspot numbers, recognizing their importance in space weather. The model addresses the complex and changing sunspot characteristics to improve forecasting accuracy. By comparing different model variations, this research identifies optimal combinations of input steps and LSTM units that enhance forecast performance while avoiding overfitting. The study showcases the capability of specific architectures concerning detail versus computational cost, using evaluation metrics such as RMSE, MAE, MAPE, and R2. Considering factors like limited data availability and the complexity of solar phenomena, the ConcaveLSTM model could be a valuable tool for predicting solar activity. This research advances understanding of space weather forecasting through machine learning and offers guidance for further model development and future investigations.
Weather Forecasting in Denpasar City Using Stacked Long Short-Term Memory Algorithm (LSTM) Hidayatulloh, M. Riyan; Diqi, Mohammad; Wijaya Sugiarto, R. Nurhadi
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/408j8q02

Abstract

Forecasting the weather is essential to sustaining everyday human activities, particularly in industries like tourism, agriculture, and transportation. The effects of extreme weather events can be lessened by timely and accurate weather forecasts. This research suggests a Denpasar City weather forecasts are made utilizing a deep learning technique and the Stacked Long Short-Term Memory architecture. The four main parameters of the model— temperature, humidity, wind speed, and pressure—were trained using historical weather data spanning 1990 to 2020. A sliding window method was used to organize the dataset into time-series sequences after it had been preprocessed using normalization techniques. The Adam optimizer was used to train the model over 50 epochs with a batch size of 64. Four regression measures were used for evaluation: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The Stacked LSTM model's average MAE of 1.08, MAPE of 10.22%, RMSE of 1.93, and R2 of 0.86 demonstrate how well it captures temporal patterns and generates precise forecasts, according to the experimental data. These results show how the Stacked LSTM approach can be used to support decision-making in weather-sensitive domains and create automated weather forecasting systems.
Design of Automatic Metal and Non-metal Waste Sorting Based on Internet of Things (IoT) Diqi, Mohammad; Hasta Mulyani, Sri; Wijaya S, R Nurhadi; Junia Sipit, Marselina
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/31bqbr09

Abstract

Ineffective waste management, especially in the process of sorting between metal and non-metal waste, is still a serious environmental problem. This research aims to design and build a prototype of an Internet of Things (IoT)-based automatic waste sorting device that is able to identify and separate types of waste independently. The system uses an ESP32 microcontroller integrated with ultrasonic sensors, inductive proximity sensors, and MQ135 gas sensors, as well as the Blynk application as an IoT-based monitoring interface. The method used is Research and Development (RnD) with six stages: literature study, system design, tool making, testing, evaluation, and conclusion. The test results show that the system is able to classify metal and non-metal waste with an accuracy rate of 90%, precision reaches 85.71% for metals, and 95.45% for non-metals. The integration of IoT technology in waste sorting tools is proven to increase the efficiency of waste processing from the source and provide innovative solutions in smarter and more sustainable environmental management.
Co-Authors Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Arum Kurnia Sulistyowati Azijah, Izattul Bagus Subekti Nuswantoro Basuki, Umar Dian Rhesa Rahmayanti Dyan Avando Meliala Eka Damayanti Elisabeth Deta Lustiyati Erizal Erizal Farida Nur Aini Fathinah, Azzah FAUZIYYAH, ANNUR HAFIZAH, IDA Hamzah Hamzah Hamzah Hasta Mulyani, Sri Hidayatulloh, M. Riyan Husna Arwa Salsabil I Wayan Ordiyasa Ikhwan Mustiadi Indra Listiawan Jelina Imlabla Junia Sipit, Marselina Kristian, Tadem Vergi Lestari, Sri Listiawan, Indra Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mujastia Muhdalifah Noverianus, Noverianus nur aini, farida Ordiyasa, I Wayan Putra Wanda Putra Wanda, Putra Romana Herlinda Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sri Lestari Sriwidodo Sriwidodo, Sriwidodo Subandi, Yeyen Sugeng Winardi Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Widyaningrum, Rini Wijaya S, R Nurhadi Wijaya Sugiarto, R. Nurhadi Wijaya, Nurhadi Wiratma, Harits Dwi Zaidir