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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 897 Documents
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Penelitian Pupuk Biofertilizer Berbahan dasar Kulit Buah Nanas dan Abu Rumput Gajah dengan pengaruh Berat bahan dan Durasi fermentasi Dian Pratiwi Tejo Kusumo; Dimas Arnanda; Kindriari Nurma Wahyusi; Dyah Suci Perwitasari; Retno Dewati
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6013

Abstract

This research develops a biofertilizer production method using pineapple peel waste and elephant grass ash as raw materials. Pineapple peel contains nutrients that can improve soil fertility, while elephant grass, commonly used as livestock feed, has high silica content beneficial to the soil when used as fertilizer. The research aimed to produce a biofertilizer and examine the effects of material weight and fermentation duration on the levels of Nitrogen (N), Phosphorus (P), and Potassium (K), assessing whether the results meet Indonesian National Standard (SNI) fertilizer requirements. The fermentation process lasted approximately 35 days. The fermented biofertilizer was analyzed using the Micro Kjeldahl method for Nitrogen, UV-Vis Spectrophotometry for Phosphorus, and Atomic Absorption Spectrophotometry (AAS) for Potassium. The results showed the highest concentrations of Nitrogen (2.98%), Phosphorus (2.43%), and Potassium (3.39%) were obtained using 40 grams of elephant grass ash and 35 days of fermentation. The analysis indicated that increasing the amount of elephant grass ash and the fermentation duration resulted in higher N, P, and K levels. These findings align with SNI fertilizer standards, highlighting the potential of using sustainable and easily accessible materials to enhance organic fertilizer production efficiency.
Comparing Algorithms in Sentiment Analysis on DUKCAPIL App Reviews on Playstore Using Ensemble Learning Methods Putu Putrayasa; Ema Utami; Robert Marco
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6020

Abstract

The development of information and communication technology has significantly influenced public services, particularly through the adoption of mobile applications like DUKCAPIL, which simplifies access to population administration services. This study aims to analyze sentiment regarding the application by employing ensemble learning techniques and the SMOTE method to address data imbalance. The Extra Trees algorithm is compared against nine other algorithms, including Random Forest, Gradient Boosting, and LSTM. Extra Trees achieves the highest accuracy of 95.29% and outperforms in precision, recall, and F1-score. Deep learning models showed improved accuracy from 76.34% in the initial epoch to 91.56% in the final epoch. XGBoost and Random Forest also demonstrated strong performances, with accuracies of 90.55% and 92.66%, respectively. The results underline the superiority of Extra Trees in terms of stability and accuracy while highlighting the potential of deep learning for model enhancement. These findings provide valuable insights for the development of mobile application-based public services.
Analysis of Cost Budget Plans in Disaster Logistics Warehouse Planning of the Badan Penanggulangan Bencana Daerah (BPBD) Bantul Regency, D.I. Yogyakarta Alfinur Insaniyati Umi Sa'adah; Sely Novita Sari; Rizal Maulana
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6033

Abstract

From the various roles of the Bantul Regency BPBD in managing disaster preparedness and response in the Bantul area, which is known to be prone to disasters, the construction of a disaster logistics warehouse is needed to support the implementation of disaster management. To ensure the effectiveness and efficiency of the construction of the warehouse, an analysis of the Cost Budget Plan (CBP) is needed. The purpose of this study is to analyze the costs needed to complete the construction of the Bantul Regency BPBD logistics warehouse. The types and sources of data used are secondary data in the form of floor plan drawings, cut drawings, and detailed drawings. The method carried out is by analyzing the price of the unit of work using the applicable units of goods and services. CBP is obtained from the product of the volume of work multiplied by the result of the unit price of work. CBP analysis on the Bantul BPBD Logistics Warehouse Planning was obtained of Rp 20,026,590,027. The results of this study help to ensure appropriate budget allocation, prevent cost overruns, and become the main guideline in project implementation to support effective disaster response by BPBD.
Crawler Crane Productivity Analysis on Erection Girder Work for Toll Road Construction Solo - Yogyakarta Astuti Umasugi; Sely Novita Sari; Rizal Maulana
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6034

Abstract

The construction of the Solo–Yogyakarta Toll Road aims to enhance connectivity between Solo and Yogyakarta and reduce traffic congestion. This study examines the productivity of crawler cranes in girder erection work to provide recommendations for optimizing crane usage in similar projects and improving construction efficiency. A quantitative approach was used, analyzing technical parameters such as lifting capacity, load charts, and operational safety, in accordance with SNI 6910-2022 standards. The findings revealed that the IHI CCH2800 crawler crane, with a maximum capacity of 280 tons, successfully handled a total load of 44.61 tons using a lifting frame. With a Dynamic Amplification Factor (DAF) of 1.05, the crane achieved a safe lifting capacity of 46.84 tons. The optimal configuration included a boom length of 33.50 meters, a lifting radius of 13 meters, and a sling capacity of 182.72 tons, resulting in a maximum lift capacity of 82.3 tons. This study highlights the importance of accurate lifting radius calculations and load chart optimization to ensure productivity and operational safety. The findings offer practical recommendations for application in future large-scale infrastructure projects.
Innovative Transformation of Analog to Digital Direct Shear Test Tool Based on Microcontroller with Wireless Data Acquisition System Maskur Efendi; Eko Setyawan
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6051

Abstract

Direct Shear Test (DST) is an important method for measuring soil shear strength in civil engineering applications. However, analog DST tools have limitations, such as low accuracy, low efficiency, and the need for manual data processing. This study aims to convert analog DST tools into digital ones using Arduino UNO R4 WiFi-based microcontroller technology and a wireless data acquisition system. This innovation replaces analog dial indicators with digital dial indicators to record shear force and horizontal displacement data automatically and in real-time. This system is designed to generate automatic test reports in the form of tables, graphs, and soil parameters, such as cohesion and angle of internal friction. The results show an accuracy rate of up to 90% compared to test results with commercial digital DST, better operational efficiency with only one operator, and a 30% reduction in testing time compared to using analog DST. With an implementation cost of less than 10% of commercial digital DST, this solution offers affordable modern testing technology for laboratories with limited budgets. This article discusses the technical design, hardware implementation, software development, and performance analysis of the resulting tool.
Implementation of Raspberry Pi PICO as PLC for Monitoring and Control in Swiftlet Cultivation Based on Weintek HMI Satrio Sarwo Mumpuni; Rini Puji Astutik
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6054

Abstract

Swiftlet farming is one of the promising cultivation efforts for breeders because it can generate multiple and sustainable profits. Large profits will have an impact on the economy of the community, especially swiftlet breeders. The use of technology to provide ideal conditions for swiftlet farming will greatly affect the quality of the swiftlet nests. The use of technology in providing an ideal environment for swiftlet cages cannot be separated from a system that is designed and reliable in extreme conditions. Raspberry Pi Pico which has an ARM Cortex processor is one solution to the technology used. The implementation of Raspberry Pi Pico into a PLC is one alternative in the development of technology in swiftlet farming. The Raspberry Pi Pico PLC was chosen because it is cheap and has been proven to be reliable in the industrial world. Control of the swiftlet farming room with a room temperature of 25 - 27 ℃ and humidity of 80 - 90%. The LM35 sensor with ladder programming using raspberry pi pico compared to sensors on the market, namely the HTC-1 digital thermometer, has an error of 2.02%, and testing of the soil moisture sensor which has been tested with an error of 1.31%. HMI wientek can display temperature and humidity well and has industrial standard reliability that can be used nonstop 24 hours. The use of raspberry pi pico as a PLC has many advantages and is easy to use because it can use the Mitsubishi PLC program, namely GX Work2.
Implementation of the Convolutional Neural Network (CNN) Algorithm for Pest Detection in Green Mustard Plants Gilang Wiwaha Soekarno; Agus Suhendar
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6095

Abstract

Green mustard plants are of significant economic importance, making effective pest management essential. This study employed the Convolutional Neural Network (CNN) algorithm to detect pests on green mustard leaf images. The dataset, comprising 96 test images, was divided into two categories: pest-infested and healthy leaves. Using the NasNet Mobile architecture, the model was trained over 10 epochs with the Adam optimizer, achieving a training accuracy of 94.99% and a validation accuracy of 98.00%. Results indicate that CNN combined with NasNet Mobile effectively identifies pests, providing a robust and practical solution to enhance agricultural productivity and mitigate crop losses caused by pests. This study demonstrates the potential of leveraging deep learning for agricultural advancements, particularly in addressing pest-related challenges efficiently.
Analysis of Occupational Safety And Health in The Construction Project of Integrated Lecture Building Dimas Aji Purnomo; Harliwanti Prisilia
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6108

Abstract

Occupational safety and health (OSH) is an important aspect in the implementation of construction projects, especially building projects that involve various high-risk activities. This research aims to analyze the implementation of the OHS system in building projects, identify potential hazards, and evaluate the effectiveness of mitigation efforts that have been made. The research method used is a quantitative and qualitative approach through field observations, interviews with workers, and analysis of related documents. The results show that the implementation of OHS still faces a number of challenges, such as lack of compliance with safety procedures, limited OHS facilities, and low levels of worker awareness of the importance of OHS. Nevertheless, some mitigation efforts, such as regular training and the use of personal protective equipment (PPE), have proven effective in reducing the risk of work accidents. This study recommends increased supervision, provision of adequate OHS facilities, and educational campaigns to build a safety culture in the work environment. The findings are expected to make a positive contribution to improving occupational safety and health on construction projects in Indonesia.
Proximate Analysis of Donuts Substituted with Corn Cob Flour (Zea mays) Sufrotun Khasanah; Mahfud Nugroho; Fitria Yuni Astuti
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6122

Abstract

Corn cobs are agricultural waste that contain nutrients that are good for humans. Corn cobs have high crude fiber, so they are good for health and safe to consume. One of them that can be used as a processed food product is donuts. Wheat flour is the raw material for making donuts, which is imported from various countries. Therefore, the use of wheat flour must be reduced by focusing on components that are abundant in Indonesia and have high nutritional content. The aim of this research is to analyze nearby donuts produced from wheat flour substituted for corn cob flour. Donuts were made with varying concentrations of 5%, 10%, and 15% corncob flour addition. The parameters tested in proximate analysis are water content, ash content, protein, fat, and carbohydrates. The research results showed that the values ​​of water content, ash content, protein, and fat tended to increase along with increasing the number of corn cobs in the donut formulation. Meanwhile, the carbohydrate content decreases with increasing corncob flour. This donut proximate analysis is in accordance with SNI 01-2000 standards. Donuts made with a higher percentage of corncob flour will be tastier.
Analisis Sentimen Tweet untuk Mendeteksi Keinginan Bunuh Diri menggunakan Pendekatan Machine Learning pada Data Besar Noviyanti. P; Candra Gudiato; Listra Frigia Missianes Horhoruw
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6154

Abstract

Suicidal ideation is a serious mental health problem and is often difficult to detect in its early stages. Social media, especially Twitter, is one of the platforms widely used by individuals to express their feelings and emotional conditions, including expressions of suicidal ideation. This study aims to develop a machine learning model that can analyze the sentiment of tweets related to suicidal ideation using big data. The data used in this study consisted of tweets that had been processed for sentiment analysis, which were then classified into three sentiment categories, namely positive, negative, and neutral. The machine learning model applied was Naive Bayes. The results of the model evaluation showed that this model had an accuracy of 72%, with precision and recall values varying depending on the sentiment category. The highest precision was recorded in the negative and neutral categories (0.91), while the highest recall was recorded in the positive category (0.97). This study provides insight into the potential use of machine learning-based sentiment analysis to detect signs of suicidal ideation through big data from social media that can help in early detection of mental health problems.

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