cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mochamad Sulaiman
Contact Email
m.sulaiman@uniramalang.ac.id
Phone
+6282331527189
Journal Mail Official
m.sulaiman@uniramalang.ac.id
Editorial Address
Fakultas Sains dan Teknologi Universitas Islam Raden Rahmat Malang Jl. Raya Mojosari 02 Kepanjen-Malang
Location
Kota malang,
Jawa timur
INDONESIA
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
 54   55   56   57   58 
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

UMKM IBS merupakan industri rumahan yang memproduksi sofa bayi, perlengkapan tidur bayi, dan produk lainnya dengan menggunakan bahan baku kain katun. Proses pemotongan kain sering kali menghasilkan cacat, yang dapat terjadi karena beberapa faktor, termasuk masalah yang berhubungan dengan mesin. Sebuah penelitian dilakukan untuk mengevaluasi kinerja mesin pemotong dengan menggunakan metode OEE dan FMEA. Analisis OEE menunjukkan skor 81,56%, di bawah standar yang diinginkan yaitu 85%. Selain itu, penelitian ini mengidentifikasi enam kerugian utama, termasuk kerusakan, penyetelan dan penyesuaian, penurunan kecepatan, penghentian produksi, dan cacat. Analisis FMEA menunjukkan Risk Priority Number (RPN) sebesar 392 karena kurangnya fokus dan pemahaman pada faktor manusia. Untuk meningkatkan efektivitas IBS UMKM, diperlukan tindakan perbaikan dalam hal pemeliharaan mesin dan kewaspadaan tenaga kerja.
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

Penentuan kematangan buah kelapa sawit sangat penting untuk meningkatkan kualitas dan kuantitas produksi minyak kelapa sawit. Penelitian ini mengkaji penggunaan teknologi deep learning untuk mengklasifikasikan kematangan kelapa sawit melalui Systematic Literature Review (SLR). Metode penelitian yang digunakan adalah Systematic Literature Review (SLR) yang melibatkan analisis 35 jurnal dari Scopus dan Google Scholar dari tahun 2020 hingga 2024, dengan fokus pada kumpulan data, algoritma, lokasi kumpulan data, dan metode pengukuran kinerja model. Hasilnya menunjukkan bahwa ANN dan CNN adalah algoritma yang paling banyak digunakan, dengan penggunaan masing-masing 16% dan 10%. Akurasi, presisi, perolehan, dan skor F1 adalah metrik kinerja yang paling umum. Penelitian di masa depan harus fokus pada peningkatan generalisasi model dan mengintegrasikan data dari berbagai sumber untuk meningkatkan akurasi klasifikasi, tujuannya untuk berkontribusi pada klasifikasi kematangan minyak sawit dan membantu industri meningkatkan efisiensi dan kualitas produksi.
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 merupakan perusahaan jasa yang bergerak di bidang pengolahan air. Perusahaan ini mengalami banyak masalah dalam proses produksi dan tidak ada rencana mitigasi yang terstruktur. Tujuan dari penelitian ini adalah untuk mengetahui risiko-risiko yang diperkirakan ada atau sudah ada dan memberikan rencana mitigasi. Penelitian ini menggunakan pendekatan SCOR untuk pemetaan dan metode House Of Risk untuk menganalisis dan memberikan mitigasi risiko. Setelah itu dilakukan analisis risiko pada House Of Risk fase 1 yang menghasilkan 35 Risk event & 30 Risk Agent dengan 5 hasil prioritas risiko yang didapat dari estimasi ARP diantaranya A16, A28, A26, A10, A18, sedangkan untuk preventive action yang didapat dari estimasi AHP sebanyak 8 yaitu PA1 dengan nilai 0, PA2 dengan nilai 26, PA3 dengan nilai 0,20, PA2 dengan nilai 0,17, PA4 dengan nilai 0,13, PA5 dengan nilai 0,12, PA6 dengan nilai 0,08, PA7 dengan nilai 0,03, PA8 dengan nilai 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

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

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%.

Page 56 of 90 | Total Record : 897

 54   55   56   57   58 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026 Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025 Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025 Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025 Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025 Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024 Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024 Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024 Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024 Vol 7 No 4 (2023): G-Tech, Vol. 7 No. 4 Oktober 2023 Vol 7 No 3 (2023): G-Tech, Vol. 7 No. 3 Juli 2023 Vol 7 No 2 (2023): G-Tech, Vol. 7 No. 2 April 2023 Vol 7 No 1 (2023): G-Tech, Vol. 7 No. 1 Januari 2023 Vol 6 No 2 (2022): G-Tech, Vol. 6 No. 2 Oktober 2022 Vol 6 No 1 (2022): G-Tech, Vol. 6, No. 1, April 2022 Vol 5 No 1 (2021): G-Tech, Vol. 5, No. 1, Oktober 2021 Vol 4 No 2 (2021): G-Tech, Vol. 4, No. 2, April 2021 Vol 4 No 1 (2020): G-Tech, Vol. 4, No. 1, Oktober 2020 Vol 3 No 2 (2020): G-Tech, Vol. 3, No. 2, April 2020 Vol 3 No 1 (2019): G-Tech, Vol. 3, No. 1, Oktober 2019 Vol 2 No 2 (2019): G-Tech, Vol. 2, No. 2, April 2019 Vol 2 No 1 (2018): G-Tech, Vol. 2, No. 1, Oktober 2018 Vol 1 No 2 (2018): G-Tech, Vol. 1, No. 2, April 2018 Vol 1 No 1 (2017): G-Tech, Vol. 1, No. 1, Oktober 2017 More Issue