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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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Analisis Efektivitas Mesin Pemotong Pada Kain Kapas Menggunakan Metode OEE dan FMEA di UMKM IBS Moh Ilham Alfarisi; Deny Andesta
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5048

Abstract

IBS MSME is a home industry that produces baby sofas, baby bedding, and other products using cotton fabric as raw materials. The fabric cutting process often results in defects, which can occur due to several factors, including machine-related problems. A study was conducted to evaluate the performance of the cutting machine using the OEE and FMEA methods. The OEE analysis showed a score of 81.56%, below the desired standard of 85%. In addition, the study identified six major losses, including breakdowns, tuning and adjustment, speed drop, production stoppage, and defects. FMEA analysis showed a Risk Priority Number (RPN) of 392 due to a lack of focus and understanding on human factors. To improve the effectiveness of MSME IBS, corrective actions are needed in terms of machine maintenance and labor vigilance.
Metode Klasifikasi Kematangan Tandan Buah Segar Kelapa Sawit: Sebuah Tinjauan Sistematis Nurita Evitarina; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5050

Abstract

Determining the maturity of palm oil fruit is very important to improve the quality and quantity of palm oil production. This research examines the use of deep learning technology to classify oil palm maturity through a Systematic Literature Review (SLR). The research method used is a Systematic Literature Review (SLR) which involves analysis of 35 journals from Scopus and Google Scholar from 2020 to 2024, with a focus on datasets, algorithms, dataset locations, and methods for measuring model performance. The results show that ANN and CNN are the most widely used algorithms, with usage of 16% and 10% respectively. Accuracy, precision, recall, and F1 score are the most common performance metrics. Future research should focus on improving model generalization and integrating data from multiple sources to improve classification accuracy, the aim of which is to contribute to palm oil maturity classification and help the industry improve production efficiency and quality
Analisis Risiko Pada Proses Produksi Dengan Menerapkan Metode House of Risk, AHP dan Pendekatan SCOR Pada PT XYZ Mohammad Bima Ghozali; Hidayat Hidayat; Yanuar Pandu Negoro
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5051

Abstract

PT XYZ is a service company engaged in water treatment.This company is experiencingmany problems in the production process and there is no structured mitigation plan. The aim of this research is to determine the risks that are estimated to exist or have already existed and provide mitigation plans. This research applies the SCOR approach for mapping and the House Of Riskmethod for analyzing and providing risk mitigation. After that, risk analysis was carried out in House Of Riskphase 1 which resulted in 35 Risk events & 30 Risk Agents with 5 priority risk results obtained from ARP estimates including A16, A28, A26, A10, A18, while for preventive The actions obtained from the AHP estimation were 8, namely PA1 with a value of 0.26, PA3 with a value of 0.20, PA2 with a value of 0.17, PA4 with a value of 0.13, PA5 with a value of 0.12, PA6 with a value of 0, 08, PA7 with a value of 0.03, PA8 with a value of 0.02.
Implementasi Metode Profile Matching Untuk Menentukan Posisi Ideal Pemain Sepakbola Dwi Kurniawan Saputro; Nurchim Nurchim; Bondan Wahyu Pamekas
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v8i4.5052

Abstract

Positioning in football is a factor that must be considered, because a game can run according to the tactics that have been made by the coach if the player's position is in accordance with his abilities and character.But in determining the position of the player requires sharp instincts and structured training so that the ideal position for each player is found. This research aims to provide assistance to coaches to determine the ideal position of players by creating asystem that can strengthen decisions and increase confidence in placing players in certain positions. This system uses the Profile Matching method, which uses the ideal value for each criterion and also the individual value of each player as a comparison.This research produces a system that shows the ideal position of the player with test results from 15 players resulting in a percentage of 80% match between the initial position and the recommended position.
Systematic Literature Review : Klasifikasi Tingkat Kematangan Buah Pisang Suhendri; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5059

Abstract

Banana ripeness is an important factor that determines the quality, taste and shelf life of the fruit. Manually determining maturity levels tends to be subjective and inconsistent, so a more accurate and efficient automatic system is needed. This research conducted a SLR to evaluate image processing and machine learning techniques in banana ripeness classification CNN is proven to be the most dominant and effective method, with significant accuracy results. Other methods such as kNN, Fuzzy Logic, and ANN also show great potential. The main challenges in developing classification models include image data variability, dataset limitations, and hardware limitations. Recent trends include the use of HSI and multimodal approaches to improve accuracy. Suggestions for future research include collecting larger and more diverse datasets, using data augmentation techniques, exploring HSI sensing, and validating models under real conditions. Thus, this research is expected to make a significant contribution in the development of an automatic system for banana ripeness classification, which can be applied in the agricultural and food industries.
Sistem Rekomendasi Pemilihan Menu Brownies Cinta dengan Metode Content Based Filtering Fauziah Fanny; Rudi Susanto; Faulinda Ely Nastiti
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5065

Abstract

Culinary businesses in Indonesia, such as Brownies Cinta Karanganyar, face challenges in helping consumers choose products with the best taste. This study aims to implement a Brownies Cinta menu recommendation system using the Content-Based Filtering method with the cosine similarity algorithm. The system was developed through the SDLC Waterfall stages: planning, design, implementation, testing, and maintenance. The TF-IDF algorithm is used to calculate the initial value, which is then processed with cosine similarity to produce accurate recommendations. The test results show that the "Lapis Kukus Fruity (Regular)" and "Brownies Oven Almond (Regular)" menus have the highest cosine similarity values, reaching 0.950. In contrast, the D19 document with the lowest cosine similarity value is considered irrelevant. In conclusion, the developed content-based recommendation system can provide the best and most relevant recommendations to users. The use of the TF-IDF and cosine similarity algorithms has proven to be accurate, increasing consumer satisfaction in choosing products. This system helps Brownies Cinta consumers determine the products they want to buy, facilitating the menu selection process.
Metode Klasifikasi Tingkat Kematangan Buah dan Sayuran : Tinjauan Sistematis Rama Saktriawindarta; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5067

Abstract

Maturity classification of fruits and vegetables is important to ensure product quality in the agricultural industry. The aim of this research is to optimize harvest and distribution times using deep learning and machine learning methods. A Systematic Literature Review (SLR) was used to identify effective classification methods and models. The dominant method is image processing (65%), followed by machine learning (50%) and deep learning (42.5%). Models such as CNN, AlexNet, and ResNet-50 show high accuracy. Performance evaluation uses metrics such as accuracy, precision, recall, and F1-score. To improve accuracy, future research is recommended to collect more diverse datasets and use hybrid methods. Development of computing infrastructure and workforce training are also necessary for the application of this technology in the agricultural industry.
Lessons Learned from Building Smart Cities: A Systematic Literature Review of Case Studies in Singapore and Seoul Dasmond Tan; Johan Setiawan; Sheera Anela; Shyfa Ariesta Rustian
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v8i4.5068

Abstract

This systematic literature review explores the characteristics and criteria essential for smart city development. The study adheres to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework and employs a meticulous approach to select relevant articles from the Scopus database. The inclusion criteria ensure that all selected articles were published between 2019 and 2023, are written in English, and originate from journals ranked within the Q1-Q4 quartiles of Scopus, thus guaranteeing the quality and relevance of the reviewed literature. This review probes into how the characteristics of residents and infrastructure motivate cities to develop their smart city initiatives. The study elucidates how technology digitizes various city aspects. Findings from the reviewed articles provide comprehensive insights into city development, integrating Information and Communication Technology (ICT), governance, and citizen participation in the process.
Komparasi Algoritma Machine Learning Untuk Menganalisis Sentimen Ulasan Pada Aplikasi Digital Korlantas Polri Siti Delimasari; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5089

Abstract

Korlantas Polri Digital Application is one of the mobile applications that provides ease for the public in extending the driving license. Sentiment analysis of user reviews can help korlantas polri identify public perception of the given service. The study aims to evaluate which of the five machine learning algorithms performed best from Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression in sentiment analysis. The evaluation was done by measuring accuracy, precision, recall and F1 measure. There were 10,000 reviews labelled with linguistic validation, re-processed, and word weighted after data was collected. Synthetic minority over-sampling techniques (SMOTE) are applied before data splitting for training and testing. The evaluation shows that Random Forest and SVM do the best. Random Forest has an accuracy of 90.77%, recall 90.77%, and its highest F1 rating is 90.79%. SVM has the highest precision with 91.14% among other algorithms, which shows the great potential of both of these algorítms in the analysis of sentiment reviews of digital applications Korlantas Polri.
Peningkatan Pelayanan Data Keuangan Siswa Melalui Sistem Informasi Keuangan Monte Carmelo (SIMOKAR) dengan Metode Extreme Programming Febriyanti Alwisye Wara; Henderikus Basilius Nong Muda; Lindiana Ermilinda; Conchita Junita Chandra; Theresia Wihelmina Mado; Maria Wihelmina Lodan
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5100

Abstract

SMAK Santa Maria Monte Carmelo, a school under the Santa Maria Karmel Foundation in Wairklau-Maumere, currently still uses manual recording of student financial reports in a financial book. This system faces problems such as inaccurate financial reports and frequent data loss, which results in invalid financial reports. To overcome this problem, this research aims to develop an application that helps school treasurers record payments quickly and accurately. The application designed is the Monte Carmelo Financial Information System (SIMOKAR), using the Visual Basic programming language (VB.Net) and MySQL as data storage. During its development, this application adopted the Extreme Programming method to ensure effectiveness and accuracy in recording student financial payments. The research results show that this system has succeeded in implementing automatic and efficient payment recording in accordance with black box testing.The SIMOKAR application has a user satisfaction rating of 90%.

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