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Mochamad Sulaiman
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G-Tech : Jurnal Teknologi Terapan
Published by Universitas Islam Raden Rahmat Malang
ISSN : 25808737     EISSN : 2623064X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering
Jurnal G-Tech bertujuan untuk mempublikasikan hasil penelitian asli dan review hasil penelitian tentang teknologi dan terapan pada ruang lingkup keteknikan meliputi teknik mesin, teknik elektro, teknik informatika, sistem informasi, agroteknologi, dll.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 984 Documents
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Design and Energy-Economic Evaluation of a Pellet-Solar Heating System for Single-Family Houses using DIN EN 12831 in Wagete-Papua Agustinus Giai; Ruben Mickael Kaiway; Joni Joni; Samuel Siregar
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10474

Abstract

This study presents the design and thermal analysis of a heating and ventilation system for a residential single-family house located in Wagete, Kabupaten Deiyai, Provinsi Papua Tengah, Indonesia, using the calculation procedures specified in DIN EN 12831. The design heating load was calculated to be 6,560 W, comprising transmission heat losses of 4,232 W, ventilation losses of 1,097 W, and an additional reheating capacity of 1,231 W. A pellet boiler (ETA Pellets Unit 7, 2.3–7.7 kW) combined with a flat-plate solar thermal system (4 collectors, total absorber area 9.4 m²) was selected as the heat generation system. Underfloor heating was designed for all rooms using wet-screed pipe systems (Variotherm, 16 mm aluminum composite pipes), supplemented by wall-mounted radiators in the bathroom (248 W) and shower (38 W). The annual heat demand was determined to be 17,756 kWh/a, with the solar system contributing 4,935 kWh/a (27.8%). Annual CO₂ emissions were estimated at 4,283 kg/a. The system complies with all requirements of the Energieeinsparverordnung (EnEV, 2014) and the Erneuerbare-Energien-Wärmegesetz (EEWärmeG, 2014), with a specific transmission heat loss of H'T = 0.29 W/(m²·K), well below the 0.4 W/(m²·K) limit. The methodology presented here provides a systematic, replicable framework for residential building energy system design in temperate climates, with direct relevance for energy system planning in remote highland communities in Papua Tengah, Indonesia.
Readiness Evaluation of the eSKoPpi Application for Electronic Correspondence Management System Development at DISKOMINFO Malang City Linda Suvi Rahmawati; Andri Prasetyo; Aurelia Dewi Mashinta
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10362

Abstract

Digital transformation in public sector organizations requires effective information systems to improve administrative efficiency and service quality. The Communication and Informatics Office (DISKOMINFO) of Malang City has developed eSKoPpi (Electronic System for Innovative Public Service Competition), a web-based application that provides structured workflows, role-based access control, and comprehensive system documentation. However, it does not support essential electronic correspondence management functions, including letter registration, electronic disposition, digital archiving, and document tracking. This study evaluates the readiness of eSKoPpi for further development into an Electronic Correspondence Management System. A qualitative case study approach was employed using document analysis, direct observation, and semi-structured interviews. The evaluation focused on documentation completeness, workflow suitability, role-based access control, document management capability, and organizational readiness. The results indicate that eSKoPpi provides a strong technical foundation through comprehensive documentation and an established role-based access control mechanism. Nevertheless, several correspondence-specific functionalities remain unavailable. To address these gaps, BPMN-based business process models, UML use case models, and user interface prototypes were developed. The findings demonstrate that eSKoPpi is technically ready for future enhancement and can support digital transformation initiatives in local government administration.
Application of Bacillus spp. and Potassium Fertilizer Against Corn Downy Mildew Disease Ratna Dwi Hirma Windriyati; Rifqi Adisonda
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10422

Abstract

Downy mildew is an important disease in corn cultivation caused by Peronosclerospora spp. There are many different pathogen species such as P. maydis, P. sorghi. Losses reach 50-80% in corn fields. The use of synthetic fungicides has a negative impact because it destroys natural enemies, resulting in pathogen resistance and plant residues. Balanced biological agents and fertilization are solutions to replace chemical fungicides. The aim of this study was to obtain the best dosage of Bacillus sp. and potassium fertilizer to suppress downy mildew and corn growth. The research method used a factorial Randomized Block Design (RBD). The dosage of Bacillus sp. was B0 (0 g), B1 (15 ml), B2 (20 ml). The dosage of potassium fertilizer (KCl) was 3 K0 (0 g/plant), K1 (6 g/plant), K2 (9 g/plant). There were 9 treatment combinations, 3 replications, so there were 27 treatment units. Plant height, number of leaves, temperature, humidity, disease incidence and disease intensity were observed. The treatment of the biological agent Bacillus spp. and the best potassium fertilizers are B1K0, B2K0 and B2K2 which are able to suppress the incidence of corn downy mildew disease, but are not yet able to produce plant height and number of corn leaves.
ICT-Enabled Smart Environment Development: A Systematic Review of Technologies, Functional Roles, and Environmental Outcomes Mashudah Sabilaturrizqi; Dhea Rahma Dianti; Sindy Nindia Maretha HarisTanti; Dery Ariswanto
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10427

Abstract

Information and communication technology (ICT) is increasingly used to address urban environmental challenges, yet evidence on its specific roles in Smart Environment initiatives remains fragmented. This study systematically reviews the technologies, functional roles, and environmental outcomes of ICT-enabled Smart Environment development. A systematic literature search was conducted across six academic databases for studies published from 2020 to 2025. Of 1,879 initially identified records, 30 studies met the eligibility criteria and minimum quality threshold and were synthesized thematically. The findings identify the Internet of Things as the most frequently reported technology, complemented by artificial intelligence, cloud/fog/edge computing, digital twins, blockchain, smart grids, and other digital systems. ICT contributes through six interconnected functions: environmental sensing and monitoring, data integration, intelligent analytics, decision support, resource efficiency, and governance support. The main conceptual contribution is an integrative ICT-enabled Smart Environment framework linking ICT enablers, functional roles, application domains, and environmental outcomes. Although the reviewed studies generally report positive contributions, direct and comparable evidence of environmental improvement remains limited. Future research should evaluate integrated ICT architectures using measurable sustainability indicators.
Environmental Impact Analysis of a Landfill in Segobang Village, Banyuwangi Harliwanti Prisilia; Dimas Aji Purnomo; Ratih Nurhayati
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10569

Abstract

One of the growing environmental problems in many places is waste management. Landfills that are not managed effectively can affect public health and the quality of the surrounding environment. The purpose of this study is to examine how the landfill in Segobang Village, Banyuwangi, impacts the environment. This study was conducted using qualitative descriptive approach and semi quantitative approach data were collected through documentation, interviews, and field observations. The results show that the landfill pollutes the air with unpleasant odors, contaminates the soil due to waste accumulation, and pollutes water through leachate. In addition, a decline in environmental comfort and the emergence of minor illnesses such as coughs and skin irritation are social effects experienced by the community. Among the primary causes of these environmental impacts are a poor waste management system, a lack of supporting facilities, and low public awareness regarding environmental hygiene. Therefore, proper waste management is necessary to reduce the impact of pollution through improved facilities, environmental monitoring, and community training.
Analysis and Modelling of The Sroyo Village Composite Bridge Superstructure Based on SNI 1725:2016 Bryan Septian Ari Pratama; Annisa' Carina; Donny July Prasetyo
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10588

Abstract

This study aims to analyze, model and validate the superstructure of the Sroyo Village Composite Bridge to evaluate it structural safety while validating the manual calculating against computer-based modelling. The research method used is manual calculation analysis and computer-based structural modelling with SAP2000, in accordance with SNI 1725: 2016, RSNI T-03-2005 and SNI 2847: 2019. The results of the analysis show an ultimate moment of 991.25 kNm and an ultimate shear force of 304.60 kNm. This value is below the nominal moment capacity of 1365.57 kNm and nominal shear capacity of 648.00 kNm. The highest demand capacity ratio (DCR) is found in the shear connector at 0.98, while other internal force components can maintain safety control with a value of <1.00. The deflection that occurs is 12.36 mm which meets the allowable limit of 15.00 mm. Furthermore, validation of the results of manual analysis and SAP2000 modeling shows a minimum deviation of 0.07% and a maximum deviation of only 4.54% thus indicating the validity of the calculation results. Therefore, the superstructure of the Sroyo Village Composite Bridge is declared to meet structural feasibility according to Indonesian National Standards. However, this study is limited to the superstructure analysis.
Sentiment Analysis of Student Perceptions of Generative AI using Data Augmentation and Machine Learning Models Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10613

Abstract

The development of Generative AI has significantly changed how students access information, understand learning materials, and generate ideas in higher education. Although Generative AI supports independent learning, it also raises concerns regarding overdependence, declining critical thinking skills, and academic integrity. This study evaluates the performance of sentiment analysis models using a processing pipeline that incorporates lexical, semantic, and generative data augmentation. The main challenge addressed in this study is class imbalance, particularly the limited number of negative sentiment samples compared to positive and neutral classes. This study applies an experimental quantitative approach consisting of dataset preparation, text preprocessing, data augmentation, feature extraction using TF-IDF, model training, and evaluation using Stratified K-Fold Cross Validation. The machine learning models evaluated include Multinomial Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine. The experimental results show that Linear SVM achieved the best performance, with an average accuracy of 79.07% and a weighted F1-score of 73.90%. Compared descriptively with the non-augmented baseline, Linear SVM showed an observed increase in accuracy from 65.00% to 79.07% and in weighted F1-score from 63.03% to 73.90%. Data augmentation also enabled partial recognition of minority-class sentiment, although a substantial proportion of negative and positive samples were still misclassified as neutral. These findings indicate that hybrid data augmentation can support the performance of classical machine learning models on small and imbalanced educational text datasets, particularly when combined with TF-IDF and Linear SVM. However, a post-hoc audit identified a discrepancy between the class distribution of the original dataset and that of the final processed dataset. Therefore, the observed model performance should be interpreted as the result of the overall processing pipeline rather than as the isolated effect of data augmentation.
Identifying Financial Literacy and Asset Participation Segments among Young Adults in Indonesia Using K-Means Clustering Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy; Kevin Harlis Oktaviano; Zakha Maisat Eka Darmawan
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10684

Abstract

Financial literacy is crucial in influencing asset ownership decisions among young adults; yet, the variability of financial literacy and asset involvement in Indonesia has not been adequately examined. This research seeks to categorize young Indonesian individuals based on financial literacy and asset participation using the K-Means clustering technique. The research employed a quantitative methodology, incorporating exploratory data analysis of a survey dataset comprising 952 participants and 13 variables related to financial literacy, asset involvement, demographic traits, economic education, and financial behavior. Missing values were addressed by group-based mode imputation for categorical variables and mean imputation for numerical variables, followed by encoding and data standardization utilizing StandardScaler. The ideal number of clusters was assessed by the Elbow Method, Silhouette Score, and Davies–Bouldin Index. Despite achieving the highest Silhouette Score at k = 2, the k = 9 model was chosen due to its lower Davies–Bouldin Index and its ability to enable more nuanced responder segmentation. The findings identified nine categories exhibiting varying levels of basic and advanced financial literacy, ranging from very low to very high. These findings offer significant insights for the formulation of targeted financial education initiatives and financial inclusion policies customized to the attributes of various young adult demographics.
Intelligent Energy Prediction in Smart Manufacturing Using Deep Learning Techniques Soni Prayogi; Wahyu kunto Wibowo
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10725

Abstract

The transition toward smart manufacturing requires advanced energy management strategies that leverage artificial intelligence to improve operational efficiency and sustainability. This study proposes a novel deep learning framework based on a Long Short-Term Memory (LSTM) network for analyzing and predicting energy consumption in smart manufacturing environments using real-time data acquired from Internet of Things (IoT)-enabled industrial sensors. Unlike previous studies that primarily focus on offline energy forecasting or static datasets, the proposed approach integrates temporal energy consumption patterns from heterogeneous sensor streams to support predictive energy management and dynamic load optimization. The collected data were preprocessed through normalization and feature engineering before being trained and evaluated using the LSTM model. Experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 0.84 kWh, a Root Mean Square Error (RMSE) of 2.13 kWh, and a coefficient of determination (R²) of 0.987, indicating high prediction accuracy. Furthermore, the predictive framework enables an estimated energy consumption reduction of 14.8% through proactive load scheduling. These findings demonstrate that integrating LSTM-based deep learning with IoT sensor networks provides an effective solution for intelligent energy forecasting, improves manufacturing efficiency, and contributes to sustainable industrial development.
Effect of Drying Time on the Quality of Nori Produced from Kappaphycus alvarezii Seaweed with the Addition of Moringa oleifera Leaves Mohammad Sayuti; Nur Hidayah; Randi Bokhy Syuliana Salampessy; Siti Zachro Nurbani; Adham Prayudi; Jaulim Sirait; Indra Sakti; Heny Budi Purnamasari; Wa Ode Nurhayatul Fadzilla
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10754

Abstract

The increasing demand for healthy and practical food products has encouraged the development of nori produced from Kappaphycus alvarezii as an alternative to conventional seaweed sheets. However, drying time is a critical processing factor influencing the sensory, physical, and functional quality of the final product. This study aimed to determine the optimum drying time for producing high-quality nori supplemented with Moringa oleifera leaves. A completely randomized design was applied using three drying times (13, 14, and 15 h), selected based on previous studies indicating that this range provides sufficient moisture removal while minimizing quality deterioration during nori production. The evaluated parameters included sensory characteristics, physical properties, and antioxidant activity. Drying time significantly affected (p < 0.05) all evaluated parameters. The 14 h treatment produced the highest sensory acceptance, with appearance (8.02), texture (8.24), taste (8.48), aroma (8.40), and color (8.36), closely resembling commercial nori. This treatment also provided balanced physical properties (thickness 0.23 mm and tensile strength 12.18 N) and moderate antioxidant activity (IC₅₀ = 137.27 ppm), which was superior to that of commercial nori (199.25 ppm). These findings demonstrate that drying for 14 h is the optimum condition for producing high-quality nori from Kappaphycus alvarezii and provides a practical processing reference for seaweed-processing industries and small-scale nori manufacturers utilizing locally available raw materials.

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