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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) OPSI JUITA : Jurnal Informatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Briliant: Jurnal Riset dan Konseptual Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Jurnal INSYPRO (Information System and Processing) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Industri Terintegrasi (JUTIN) Community Empowerment Jurnal Teknologi Informasi Cyberku Jurnal Algoritma Jurnal Riptek
Heru Agus Santoso
Universitas Dian Nuswantoro
Arts Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation

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Pelatihan Robot Sepak Bola Berbasis Bluetooth Untuk Siswa SLB Negeri Semarang Pambudi, Arga Dwi; Santoso, Heru Agus; Tamami, Aries Jehan; Arifin, Zaenal; Heryanto, M Ary; Rahadian, Helmy; Alfani, Wahyu; Jeffry, Muhammad; Setiadi, Kristoforus Adrian
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2259

Abstract

Minat terhadap robotika semakin meningkat di kalangan masyarakat saat ini. Robotika tidak hanya menjadi subjek hobi, tetapi juga memiliki potensi sebagai bidang karier di masa depan. Namun, aksesibilitas terhadap peluang dalam robotika sering kali terbatas bagi individu dengan kebutuhan khusus, termasuk siswa Sekolah Luar Biasa (SLB). Dari perspektif pengembangan pendidikan, robotika menawarkan kesempatan untuk meningkatkan keterampilan kritis seperti pemecahan masalah, pemrograman, dan kerja tim. Pelatihan robotika bukan hanya tentang memperoleh keterampilan teknis, tetapi juga tentang membangun rasa percaya diri dan kemampuan untuk berpartisipasi dalam aktivitas yang dianggap sulit. Melalui kerjasama dengan SLB Negeri Semarang, kegiatan pengabdian ini bertujuan untuk memberikan akses yang lebih besar kepada siswa SLB dalam bidang teknologi, khususnya melalui pelatihan robot sepak bola berbasis Bluetooth. Dengan demikian, diharapkan mereka dapat berkembang dan berkontribusi secara positif dalam masyarakat. Kegiatan ini didanai oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Dian Nuswantoro dan mencakup penyusunan materi pelatihan, demonstrasi praktis, serta pendampingan langsung. Hasilnya menunjukkan peningkatan pemahaman siswa terhadap teknologi serta keterampilan dalam merakit dan mengoperasikan robot sepak bola.
Rancang Bangun Teknologi Mesin Crumb Rubber dan Sistem Informasi Rantai Pasok untuk Mengolah Limbah Ban Bekas di Kota Semarang Wijaya, S.T, M.Sc, A.MP, Dewa Kusuma; Santoso, Heru Agus; Islahudin, Nur
Jurnal Riptek Vol 18, No 2 (2024)
Publisher : Badan Riset dan Inovasi Daerah Kota Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35475/riptek.v18i2.285

Abstract

Central Java, especially Semarang City, is one of the provincial capitals with quite high economic progress. These indicators can be seen through the high population dan the number of motorized vehicles as a mode of transportation. The high number of motorized vehicles can hurt the environment through pollution dan vehicle replacement waste. This research examines used tires as a type of waste produced from vehicle maintenance, which has become a polemic until now. This is because recycling is difficult dan has only been used as raw material for vulcanization or thrown into the environment. Through this research, the technology for processing used tire waste into crumb rubber products has become an effective response solution. Crumb rubber is a crumb rubber product that can be an industrial raw material with high economic value, where the selling price of crumb rubber is around IDR 65,000 per kg. On the other hdan, developing appropriate technology can provide added value for the environment by converting waste into raw material products to reduce impacts. Apart from that, there is added value from a social aspect because it provides new employment opportunities for related parties, namely the community as business actors who can partner with vehicle repair shops as waste providers. This research also designs a supply chain information system to support circular economic activities related to managing used tire waste into crumb rubber.
Design for Manufacturing and Assembly Optimization of Home-Scale Biodigester-Composter Using VDI 2222 and Finite Element Analysis Methods Wijaya, Dewa Kusuma; Suprijono, Herwin; Santoso, Heru Agus; Kusmiyati, Kusmiyati; Muchti, Muhammad Agusdika Ridho
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 26 No. 2 (2024): December 2024
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.26.2.157-170

Abstract

This research focused on the Design for Manufacturing and Assembly (DFMA) optimization of home-scale biodigester-composter machines. The aims to determine feasibility from technical-economic aspects. The technique was to design the machine's mechanical process, physical, and constituent components. There are two methods conducted on this research, VDI 2222 to optimize and Finite Element Analysis (FEA) to assess the optimal quality results of the machine design based on simulation analysis. This research ended with making a physical prototype of a home-scale biodigester-composter machine using the optimal design, then validating it with a working test of the machine. The results of the VDI 2222 method show an optimal design concept through the structure of the working mechanism. All its constituent components match with the ten target specifications and the machine manufacturing cost of IDR 2,393,000, as well as the assembly chart design for each constituent component. These results are also evaluated using the FEA method. The resistance value of the frame system to maximum Von Mises Stress is obtained at 128.75 MPa with a minimum value of 6.93e-04 MPa. It is concluded to be acceptable at withstanding normal and shear stresses effectively with a relatively small displacement value of 0 to 0.47 mm. The equivalent strain value results are 3.89e-09 ul to 5.83e-04 ul and safety factor value results are 1.93 to 15 ul. It can be concluded that the frame system design concept is safe.
Penerapan Antropometri Terhadap Perancangan Alat Stunting Pengukur Tinggi dan Berat Badan Anak-anak yang Ringkas dan Sistematis Rifki, Ahmad Muhson; Santoso, Heru Agus
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 2 (2025): April
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i2.43970

Abstract

Routine health checks, especially anthropometric measurements such as height and weight, are very important for the prevention and early detection of stunting in early childhood (1-5 years). Stunting caused by long-term malnutrition can inhibit physical growth and brain development in children, thus affecting their productivity in the future. However, manual measurements that are still widely used have various obstacles, such as low accuracy, potential bias, and long implementation time. Conventional measuring instruments are often not optimal due to limitations in accuracy, safety, and comfort, especially in Posyandu. Therefore, it is necessary to design a height and weight measuring instrument that is integrated, ergonomic, lightweight, safe, and attractive for children, to make it easier for Posyandu cadres and health workers to detect stunting more accurately and efficiently.
Model Prediksi Stunting Anak di Indonesia Menggunakan Extreme Gradient Boosting Aziz, Halim Al; Santoso, Heru Agus
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2289

Abstract

Stunting is a significant nutritional problem that negatively impacts children’s physical and cognitive development, especially in poor countries like Indonesia. This study used the XGBoost algorithm to examine stunting data in children under the age of five. The analysis results showed that XGBoost processed complex datasets quickly and produced accurate predictions, achieving a model performance evaluation with 86 percent accuracy, 89 percent precision, 95 percent recall, and 92 percent F1 score. This approach effectively found significant trends for early stunting identification through the utilization of body mass index (BMI) and other anthropometric data, which conventional methods failed to reveal. This study also presents opportunities for advancement in the Internet of Things (IoT) framework to improve the efficacy of real-time stunting detection systems. IoT devices provide more precise and reliable anthropometric data collection, thereby improving the efficacy of the XGBoost model in estimating stunting risk. Although IoT applications were not the primary focus of this study, its findings provide substantial contributions to the advancement of data science and technology in the healthcare sector, particularly in initiatives aimed at preventing stunting. This research offers theoretical contributions to the development of data science and health technology, as well as practical benefits in the form of data-based solutions that can be integrated into national programs to reduce the prevalence of stunting, to support more targeted nutritional interventions and improve the quality of life of children in Indonesia.
Design and manufacturing optimization of herbal drink crystallization machine using reverse engineering method Santoso, Heru Agus; Islahudin, Nur; Wijaya, Dewa Kusuma
OPSI Vol 16 No 2 (2023): ISSN 1693-2102
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v16i2.11335

Abstract

This article explains the design and manufacture of a herbal drink powder crystallization machine at Menik Jaya MSMEs. The problem with this research stems from the large number of defective products produced in the form of herbal powder that is burnt and lumpy. This is because production staff still manually stir the process continuously without stopping, causing fatigue due to work and impacting the quality of the process. So that the optimal design and manufacture of the crystallization machine is obtained where the dimensions of the machine are 130 cm high, 100 cm long and wide, machine legs height is 62 cm, stove height is 87 cm from the base and the stove pan handle width is 12 cm. Regarding engine performance, the ideal electric capacity of the motor is 60 watts with a stirrer and pan made from a combination of wood and AISI 3195 stainless steel. The pully uses 2 types of sizes, if the volume of raw material is small then a 4 cm pully is used with 10 Rpm rotation speed and 0,052 Nm torque value, while for large volumes a 6 cm pully is used with 15 Rpm rotation speed and 0,038 Nm torque value.
Performance Evaluation of Deep Learning Models for Cryptocurrency Price Prediction using LSTM, GRU, and Bi-LSTM Yanimaharta, Arya; Santoso, Heru Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7353

Abstract

Cryptocurrency price prediction poses a significant challenge in the digital finance landscape due to its high volatility and complex data patterns. Traditional statistical methods often fail to capture the nonlinear and temporal dependencies inherent in cryptocurrency price movements. This study addresses this issue by evaluating and comparing the performance of three deep learning architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (Bi-LSTM). in predicting the closing prices of Bitcoin (BTC), Ripple (XRP), and Dogecoin (DOGE). The dataset was obtained from Yahoo Finance, covering the period from January 1, 2020, to April 30, 2025. The models were evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE), with a forecasting horizon of 30 days. The results of this study indicate that the LSTM model achieved the highest accuracy for Bitcoin and Ripple, with MAPE values of 2.58% and 4.33%, respectively. Meanwhile, the GRU model demonstrated the best overall performance for Dogecoin, with RMSE (0.0131), MAE (0.0084), MAPE (4.12%), and SMAPE (4.06%). On the other hand, the Bi-LSTM model exhibited the lowest performance across all tested cryptocurrencies. These findings highlight the importance of selecting an appropriate model for developing accurate cryptocurrency price prediction systems. This study contributes to the field by providing a detailed comparative analysis of model performance across cryptocurrencies with differing volatility patterns, offering insights for developing more robust and tailored predictive systems in volatile financial environments.
Green economy empowerment for MSMEs: AI-integrated organic waste processing technology from industry and agriculture to drive innovation in alternative poultry feed products Wijaya, Dewa Kusuma; Santoso, Heru Agus; Setyaningrum, Ratih
Community Empowerment Vol 10 No 10 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.14869

Abstract

This community service activity aimed to empower Repro Micro, Small, and Medium Enterprises (MSMEs) in Semarang City to process organic waste, specifically, milk protein waste from the bread industry and banana tree waste from agriculture, into poultry feed pellet products. These products are intended to support livestock food security, environmental management, and social empowerment. The problem-solving method was implemented through an applied green economy approach, engineering a series of poultry feed pellet production machines integrated with an Artificial Intelligence (AI) system. The realization of this engineered technology series adheres to several technical standards and is designed with multi-feature mechanisms: coarse chopping, fine chopping, pulverizing, and pellet molding. The integration of the AI system serves as a virtual assistant to guide the partner in optimally composing the raw material mixture and simultaneously predict the resulting content based on quality parameters. This integration aims to enhance process effectiveness through improved quality outcomes while maintaining production efficiency. The results of the program realization show that the partner is able to utilize the technology effectively to independently produce poultry feed pellets at a cost of approximately IDR 5,850 per kg. Furthermore, the program's impact has demonstrated an ability to enhance self-reliance, competitiveness, environmental awareness, and business sustainability, thereby contributing to community welfare and advancing the people's economy at the MSME level.
Prediksi dan Optimalisasi Konsumsi Energi Smart Atmospheric Water Generator (SAWG) Menggunakan XGBoost Regression Wiradinata, Halim Jayakusuma; Santoso, Heru Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8655

Abstract

The decreasing availability of clean water has motivated the use of Smart Atmospheric Water Generator (SAWG) systems as an alternative water source, but their electrical energy consumption fluctuates with ambient conditions and operating patterns. This study develops a predictive model of SAWG energy consumption (kWh) using Extreme Gradient Boosting (XGBoost) and demonstrates a prediction-based operational optimization scheme for energy-efficient scheduling. The SAWG logging dataset (1,601 rows, 9 variables) is preprocessed through missing-value handling, numeric conversion, and noise/outlier detection, resulting in 1,313 usable records. The feature set includes environmental parameters, electrical signals, and time features: hour of day, day of week, and month. Modeling employs chronological time-based splits (80:20 as the main configuration and 60:40 as a robustness check), Time Series Cross-Validation on the training block, and hyperparameter tuning via GridSearchCV. Evaluation on the hold-out test sets shows that the model’s performance in a strict time-series setting remains limited: for the 80:20 split, the test results are approximately MAE = 23.16 kWh, MSE = 648.93 kWh², and R² = −0.22, while for the 60:40 split they are MAE = 27.21 kWh, MSE = 932.17 kWh², and R² = −1.75. Although the model cannot yet explain the overall variance of energy consumption satisfactorily, it can still be used to rank hours by predicted energy. In the prediction-based operational optimization stage, hourly model outputs are fed into a Greedy Scheduler that selects H = 8 operating hours with the lowest predicted energy. Compared with a naive schedule, which yields a total predicted energy of 47.493 kWh over the simulation horizon, the greedy schedule achieves 43.134 kWh, corresponding to an estimated saving of about 9.18%. These results indicate that prediction-based scheduling can reduce SAWG energy consumption without modifying the device hardware and can be further developed as a decision-support component for SAWG operation.
Co-Authors Achmad Naila Muna Ramadhani Achmad Zaki Adhe Irham Thoriq Adhitya Nugraha Adhitya Nugraha Akbar Aji Nugroho Akhmad Pandhu Wijaya, Akhmad Pandhu Alfani, Wahyu Amiril Mukminin Arga Dwi Pambudi Arkav Juliandri Arkav Juliandri Assiva, Mochamad Amry Atanasius Marcello Aziz, Halim Al Catur Supriyanto Catur Supriyanto Catur Supriyanto De Rosal Ignatius Moses Setiadi Dewa Kusuma Wijaya Eko Hari Rachmawanto Fahri Firdausillah Farah Zakiyah Rahmanti Herwin Suprijono Heryanto, M Ary Hidayati, Ulfa Jeffry, Muhammad Kusmiyati Kusmiyati Luh Putu Ratna Sundari Muchti, Muhammad Agusdika Ridho Muhamad Haris Zuhri Muslikh, Ahmad Rofiqul Nur Islahudin Pujiono Pujiono Purwanto Purwanto Rahadian, Helmy Rama Tri Agung Ratih Setyaningrum Rifki, Ahmad Muhson Rizaldy, Adhy Roiya Ravida Ruri Suko Basuki Rustam, Suhardi Santi, Dian Nirmala Saraswati, Galuh Wilujeng Septian Enggar Sukmana Setiadi, Kristoforus Adrian Setyadhi Putra Deriyanto Suhardi Rustam Tamami, Aries Jehan Tjetjep Witjahjono Wijaya, Dewa Kusuma Wijaya, S.T, M.Sc, A.MP, Dewa Kusuma Wiradinata, Halim Jayakusuma Yanimaharta, Arya Zaenal Arifin