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Wandi Syahindra
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arcitech.journal@iaincurup.ac.id
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Arcitech: Journal of Computer Science and Artificial Intelligence
Published by Institut Agama Islam Negeri Curup
ISSN : 29623669     EISSN : 29622360     DOI : http://dx.doi.org/10.29240/arcitech
Core Subject : Science,
Computer Science & IT
Arcitech: Journal of Computer Science and Artificial Intelligence, is an Open Access and peer-reviewed journal published by the State Islamic Institute (IAIN) Curup. This journal focuses on the field of computer science and artificial intelligence covering all aspects of information technology, computer science, computer engineering, information systems, Software Engineering and its development, software engineering Computer networks, IoT, security systems, Simulation Modeling and Applied Computing, Computing High Performance, Image and speech processing, big data and data mining, and artificial intelligence. The journal is published by Institut Agama Islam Negeri (IAIN) Curup, online and printed twice a year, in June and December.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 49 Documents
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Aplikasi Pengenalan Pola Huruf Aksara Kaganga Menggunakan Jaringan Saraf Tiruan Dengan Metode Ekstraksi Fitur Geometri Rianti, Rara Nufa
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 3 No. 2 (2023): December 2023
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v3i2.9792

Abstract

This research aims to enable the Rejang Lebong community to relearn the Kaganga script from elementary school children to the elderly. The Kaganga script is only learned through books, teenagers or adults no longer want to learn the Kaganga script, so it is important for the Rejang Lebong people to maintain their very valuable culture. The method used is a qualitative method which is a method that focuses on in-depth observation. Data collection was carried out using an observational study conducted in the form of experimental research using a case study approach per individual for 7 (seven) image inputs consisting of 28 (two eight) Kaganga script letters to 7 (seven) different people to obtain the number of executions. The expected number of Kaganga characters is 196 letters. Testing the application by applying artificial neural networks with geometric feature extraction succeeded in character recognition of images in testing with a success rate of 92%.
Implementasi Sistem Informasi Administrasi Prodi PTIK Universitas Bung Hatta Khairi, Ashabul; Diasno, Renonaldy Pratama
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 1 (2024): June 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i1.11032

Abstract

This study aims to develop a web-based administrative information system for the Program of Educational Technology and Computer Engineering at Universitas Bung Hatta, replacing the previously used manual methods. The developed system is designed to manage student thesis data, lecturer documents, and accreditation processes. The development method applied is the waterfall model, which includes stages of analysis, design, coding, testing, and maintenance. Results from validation testing indicate that the system is ready for use and suitable for further research. The information system features menus such as academic, administration, lecturers, alumni, and news, and utilizes a MySQL database. The use of responsive PHP programming facilitates data access and processing. The implementation of this system is expected to enhance the efficiency and effectiveness of administrative management and simplify the accreditation process for the PTIK program.
Rancang Bangun Sistem Informasi Inventory Barang Berbasis Web (Studi Kasus Toko Dina Beauty Care) rura, octavianus sabi; Ardiansyah, Riki
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 3 No. 2 (2023): December 2023
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v3i2.8191

Abstract

Penelitian ini menyajikan desain dan pengembangan aplikasi manajemen inventaris berbasis web untuk toko Dina Beauty Care menggunakan metode Waterfall. Latar belakang dari penelitian ini menekankan bernilainya sistem manajemen inventaris yang efisien dalam menambah efisiensi operasional, mengurangi anggaran, serta menaikkan kepuasan pelanggan dalam bisnis ritel. Metode studi yang digunakan dalam penelitian ini mengikuti pendekatan pengembangan perangkat lunak Waterfall.  Ini terdiri dari tahapan sekuensial, termasuk pengumpulan kebutuhan, desain sistem, implementasi, pengujian, dan pemeliharaan. Penelitian ini menekankan pentingnya perencanaan dan dokumentasi yang teliti pada setiap tahap proses pengembangan. Hasil dari penelitian ini memperlihatkan implementasi yang sukses dari aplikasi manajemen inventaris berbasis web untuk toko Dina Beauty Care. Aplikasi ini menawarkan fitur seperti pelacakan produk, manajemen stok, dan kemampuan pelaporan. Aplikasi manajemen inventaris berbasis web yang dikembangkan memberikan solusi efektif bagi toko Dina Beauty Care untuk menyederhanakan proses manajemen inventaris mereka. Aplikasi ini meningkatkan akurasi inventaris, mengurangi kesalahan manual, meminimalkan kehabisan stok, dan memungkinkan pengambilan keputusan yang lebih baik melalui laporan yang komprehensif. Hal ini akhirnya menyebabkan peningkatan efisiensi operasional dan peningkatan kepuasan pelanggan.
Analisis Sentimen Opini Publik terhadap Kasus Korupsi Timah di Youtube Menggunakan Metode Oversampling dan Algoritma Decision Tree Pramudiya, Relin; Kadafi, Aldo; Udjulawa, Daniel
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 1 (2024): June 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i1.10472

Abstract

This study analyzes public opinion regarding the corruption case of PT. Timah (Tbk), which caused state losses of up to IDR 271 trillion, through YouTube comments. The methods used are the Decision Tree algorithm and the SMOTE Oversampling method. A total of 2501 comments were collected and processed. The stages include data preprocessing, sentiment labeling, and model training. The results show that the use of SMOTE improves the accuracy and performance of the model. With SMOTE, the model achieves an accuracy of 56%, a precision of 0.55, a recall of 0.55, and an F1-score of 0.55, while without SMOTE, the model only achieves 54%, a precision of 0.52, a recall of 0.52, and an F1-score of 0.52. Precision, recall, and F1-score also increase when using SMOTE. This study highlights the importance of the Oversampling technique in dealing with class imbalance to improve the accuracy and sentiment analysis model. These results make a significant contribution to sentiment analysis, highlighting the role of SMOTE in overcoming class imbalance and creating a more accurate model.
Implementasi RESTful API Pada Laravel dan Simulator IoT Wokwi Untuk Pengukuran Suhu dan Kelembaban Menggunakan Metode Waterfall Ardhana, Valian Yoga Pudya; Hidayat, Muhammad Taufiq; Jannah, Miftahul; Sumiati, Sumiati; Rini, Puspita; Sari, Nila
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 3 No. 2 (2023): December 2023
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v3i2.9334

Abstract

This research discusses the implementation of RESTful API in the Laravel web development framework, along with the utilization of the IoT Wokwi simulator for monitoring temperature and humidity. The application of the waterfall development method is employed as an approach to organize the development process with clear steps in creating the temperature and humidity measurement program. The aim of this research is to create an efficient and well-structured application program interface using Laravel, while simultaneously leveraging the IoT Wokwi simulator as a tool for testing and validating the functionality of temperature and humidity measurement. The research methodology involves the use of the software development life cycle (SDLC) model, specifically the waterfall model, as an approach to the software development life cycle. The anticipated outcome of this research is to provide practical guidance for developers in creating an effectively integrated temperature and humidity measurement system using web technology and IoT.
Predicting Early Childhood Readiness to Enter Elementary School Using the Naive Bayes Classification Puspitorini, Sukma; Kahar, Novhirtamely; Kartika, Ikah
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11635

Abstract

This study aims to examine the readiness and maturity of early childhood in entering elementary school using the Naïve Bayes method. This analysis involves variables such as gender, age, aspects of physical-motor, cognitive, social-emotional development, and literacy skills which include reading, writing, arithmetic, and children's level of independence. The readiness category is classified into two classes, namely "ready" and "not ready". This prediction model is designed to provide a comprehensive understanding of the factors that affect the classification results, so that the evaluation process can be carried out in a transparent, objective, and data-driven manner. This research is expected to be a reference for other educational institutions in implementing a similar model to evaluate student readiness systematically. By adjusting variables and data according to local needs, this model has the potential to support more accurate and standardized decision-making, as well as improve the quality of early childhood preparation in entering formal education. The results show that the Naïve Bayes method is able to achieve an accuracy level of 93.33%, confirming its effectiveness in identifying early childhood readiness optimally.
Pengembangan Sistem Informasi Penjualan Online Untuk Produk Lokal Papua: Pendekatan Waterfall Petrus, Risma; Mardewi; Marhaba, Melvi
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11835

Abstract

Micro, small and medium enterprises are part of the business that has an important role in the national economy. Currently, MSMEs through government programs continue to be attempted to be run in all regions in Indonesia. One of the regions that is involved in running the MSME program is the Papua region. The purpose of the research conducted is to design a sales system for local Papuan products that can improve the economy in the Papua region from the crafts of the people in the Papua region using an online-based sales concept. The system development method used is the waterfall method which begins with the stages of needs analysis, system design, implementation, testing and maintenance. The results of the research obtained from this study are to promote local Papuan-style products from the eastern region using a website so that they can be reached by all groups. Through this research, it is obtained that by using a seller system, it makes it easier for business actors in the Papua region to market their products and through this system, they can introduce local Papuan products to a wider market, increase competitiveness and ultimately encourage the local economy, and be useful for research in the field of information technology which can then carry the diversity of local wisdom products.
Decision Support System for Plantation Land Suitability Assessment Using A Combination of AHP (Analytical Hierarchy Process) and Profile Matching Method Sahputra, Ilham; Fitria, Rahma; Sukia, Sukia
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11957

Abstract

Determining the suitability of plantation land is a crucial factor in enhancing productivity and sustainability in the agricultural sector. However, existing studies often lack comprehensive approaches that integrate both the prioritization of criteria and precise evaluation of land suitability. This study addresses this gap by developing a decision support system (DSS) for plantation land suitability using a combination of the Profile Matching and Analytic Hierarchy Process (AHP) methods. The AHP method is employed to assign weights to various criteria based on their relative importance, while the Profile Matching method evaluates land suitability based on the generated profiles.  The results indicate that this integrated approach provides accurate and detailed land suitability recommendations. Specifically, Buket Rata land is suitable for Clove (preference score: 3.821), Oil Palm, and Tea (3.596); Reulet land is suitable for Cocoa (3.22) and Coconut (3.16); Geulanggang Kulam land is suitable for Clove (3.41), Cocoa (3.35), and Oil Palm (3.29); Sawang land is suitable for Clove (3.41), Oil Palm (3.17), and Cocoa (2.99); and Pesisir Laut land is suitable for Sugarcane (3.353) and Clove (3.173). This DSS not only aids decision-makers in optimizing land use and managing sustainable plantations but also contributes to the broader field of agricultural decision-making by demonstrating the effectiveness of combining AHP and Profile Matching methods.
Sistem Informasi Pendeteksi Penyakit Pada Kucing Dengan Metode Backward Chaining Karnila, Sri; Darmawan, Algifari
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.12033

Abstract

Cats are a popular pet in Indonesia, with a significant increase in the number of owners. However, difficulties in recognizing early symptoms of diseases often lead to delayed treatment and worsen the health condition of the cats. The purpose of this study is to design a web-based information system that can help detect diseases in cats earlier. The research methodology employs Backward Chaining to detect cat diseases at an early stage. The process begins with collecting symptoms from users, matching these symptoms against a database, and backtracking to determine the likely diseases. Black box testing shows that the system functions well, while validation with entered case data indicates that the Backward Chaining method is successful in providing relevant initial action recommendations. Unlike previous studies, which generally only developed systems based on symptom lists without deep inferential capabilities, this research fills a gap by integrating a more systematic backtracking mechanism through the Backward Chaining method. This approach allows the system to deliver more accurate and specific diagnoses based on a combination of symptoms.
Penerapan Predictive Analytics untuk Analisis Faktor-faktor yang Mempengaruhi Performa Akademik Siswa Yanuarini Nur Sukmaningtyas; Makhfuddin Akbar, Ronny; Rohma Utami Asyafiiyah, Gita
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.12048

Abstract

Education in Indonesia currently faces several challenges, particularly the inequality of educational facilities in rural areas, leading to lower academic achievement compared to urban students. This study differs from previous research that focused solely on machine learning with academic data. Using a data-driven predictive analytics approach, the research aims to analyze factors influencing student academic performance, such as study hours, sleep hours, previous scores, and extracurricular involvement. Several machine learning algorithms including Linear Regression, Support Vector Regression (SVR), Random Forest, K-Nearest Neighbors (KNN), and XGBoost were employed to build the prediction model. The results indicated a significant correlation of 0.92 between previous scores and academic performance. Among the five algorithms, the XGBoost model demonstrated superior performance compared to the others. This highlights the effectiveness of the XGBoost model in predicting factors that affect students' academic performance and its potential as a tool for educators to develop more effective learning strategies, ultimately aiming to enhance students' academic achievements significantly.

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