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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 370 Documents
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Pengembangan Chatbot Pada Platform Telegram Sebagai Media Informasi Seputar Handphone Umam, Arfiyan khusnul; Wijayanti, Esti; Chamid, Ahmad Abdul
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2150

Abstract

Di tengah kompleksitas pasar teknologi yang terus berkembang, generasi muda sering menghadapi tantangan dalam menemukan informasi yang relevan, terutama terkait perangkat seluler. Untuk mengatasi masalah ini, penelitian ini bertujuan mengembangkan Chatbot Tanya Phone, sebuah solusi interaktif yang dirancang untuk memberikan informasi spesifikasi, harga, dan ulasan produk kepada pengguna Telegram. Proses pengembangan chatbot ini mencakup analisis menyeluruh terhadap kebutuhan pengguna, perancangan alur percakapan yang intuitif, serta pengembangan berbasis API Telegram untuk memastikan integrasi yang efisien dan responsif.Implementasi sistem diharapkan dapat memberikan respons yang cepat dan akurat, membantu pengguna dalam memahami informasi penting terkait perkembangan teknologi di pasar handphone saat ini. Metode pengujian yang digunakan dalam penelitian ini adalah black box testing, yang bertujuan untuk memastikan bahwa semua fitur chatbot berfungsi sesuai dengan ekspektasi dan memenuhi kebutuhan pengguna. Selain itu, proses pengujian juga mengidentifikasi beberapa aspek yang memerlukan penyempurnaan guna meningkatkan kinerja chatbot secara keseluruhan. Hasil penelitian menunjukkan bahwa Chatbot Tanya Phone tidak hanya mampu memberikan umpan balik secara real-time, tetapi juga meningkatkan pemahaman pengguna terkait teknologi, memudahkan pencarian informasi, serta memberikan kontribusi positif bagi generasi muda dalam menghadapi perkembangan teknologi yang semakin dinamis di era digital saat ini, serta membantu mereka dalam membuat keputusan yang lebih baik dan memperkuat keterampilan literasi digital mereka untuk beradaptasi dengan perubahan teknologi yang cepat.
Analisis Prediksi Penjualan Suku Cadang Motor dengan Metode Monte Carlo Rais, Edo Rinaldi; Sovia, Rini; Sumijan
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2231

Abstract

Peramalan penjualan merupakan salah satu aspek penting dalam strategi manajemen bisnis, terutama dalam industri otomotif yang memiliki pola permintaan yang fluktuatif. Manajemen stok yang tidak optimal dapat menyebabkan overstock atau stockout, yang berdampak pada efisiensi operasional dan kepuasan pelanggan. Penelitian ini bertujuan untuk menerapkan metode Monte Carlo dalam memprediksi penjualan suku cadang motor di Bengkel Ilham Motor, guna meningkatkan akurasi prediksi dan membantu optimalisasi pengelolaan persediaan barang. Metode penelitian ini menggunakan data historis penjualan tahun 2024, yang dianalisis melalui beberapa tahapan: penentuan distribusi probabilitas, pembangkitan angka acak, simulasi Monte Carlo, dan validasi hasil prediksi. Implementasi metode ini dikembangkan dalam sistem berbasis web, menggunakan PHP sebagai bahasa pemrograman dan MySQL sebagai basis data. Hasil penelitian menunjukkan bahwa metode Monte Carlo mampu memberikan tingkat akurasi prediksi yang tinggi, dengan rincian sebagai berikut: oli (95,33%), kampas rem (99,59%), lampu depan (97,27%), saringan udara (97,53%), busi (95,78%), dan sil karet (97,32%). Prediksi yang dihasilkan memungkinkan bengkel untuk menentukan jumlah stok yang lebih optimal, sehingga dapat menghindari kelebihan maupun kekurangan persediaan. Selain itu, sistem berbasis web yang dikembangkan terbukti dapat mempercepat analisis data dan membantu dalam pengambilan keputusan bisnis yang lebih akurat. Kesimpulan dari penelitian ini adalah bahwa metode Monte Carlo dapat diandalkan sebagai pendekatan prediktif dalam perencanaan stok suku cadang motor. Untuk pengembangan lebih lanjut, disarankan agar model ini dikombinasikan dengan teknik machine learning atau mempertimbangkan faktor eksternal seperti tren pasar dan harga bahan baku guna meningkatkan akurasi prediksi.
Prediksi Safety Stock Produk Filter Oli Sepeda Motor Berbasis Demand Response (DR) - ARMA Tendean, Sandi; Tjen, Jimmy; Iskandar, Riyadi Jimmy
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2282

Abstract

Manajemen rantai pasokan merupakan hal krusial yang dibutuhkan dalam menjaga persediaan suatu produk supaya tetap tersedia selama masa tunggu. Hal ini bertujuan untuk menjaga keberlanjutan suatu bisnis sehingga penjualan produk tersebut tidak terganggu dengan permasalahan kurangnya persediaan. Namun, metode prediksi konvensional seperti ARMA-klasik dan ARMA-GARCH seringkali kurang akurat pada data riil yang bersifat sparse yang didominasi nilai nol dan fluktuatif. Penelitian ini bertujuan untuk menggagas sebuah metode Auto Regressive Moving Average (ARMA) baru yang menggabungkan konsep demand response dengan analisis galat yang bernama Demand Response-ARMA (DR-ARMA). Metode ini dikembangkan melalui tiga tahap, yaitu penurunan matematis berbasis RMSE dan analisis tren, adaptasi model untuk data sparse, dan validasi menggunakan data primer penjualan sparepart filter oli dari CV di Kalimantan Barat selama 60 hari. DR-ARMA mengoptimasi prediksi ARMA berdasarkan pada tren penjualan serta mengontrol ketidakpastian prediksi dengan memanfaatkan analisis galat, supaya kesalahan prediksi dapat berkurang selama perhitungan safety stock. Simulasi numerik dilakukan pada data penjualan filter oli dari sebuah perusahaan yang ada di Kalimantan Barat. Hasil simulasi menunjukkan bahwa metode DR-ARMA dapat memprediksi penjualan filter oli dengan akurasi 80%, lebih tinggi dibandingkan metode prediksi lainnya seperti ARMA-Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (74%) dan ARMA-klasik (57%). Metode DR-ARMA juga dapat digunakan untuk memprediksikan safety stock untuk 60 hari kedepan dengan tingkat kesalahan prediksi sekitar 17%. Hal ini menunjukkan bahwa metode DR-ARMA cocok digunakan untuk memprediksikan safety stock dari data yang bersifat sparse. Metode DR-ARMA dapat membantu pengguna dalam mengatur jumlah persediaan barang yang dibutuhkan tanpa perlu melakukan pengisian gudang secara berlebihan.
Demand Prediction and Apparel Production Management Using AI-Based Decision Tree Ariyanto, Iqbal Haqiqi; Chamid, Ahmad Abdul; Fiati, Rina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2325

Abstract

The apparel industry faces significant challenges in demand forecasting due to market volatility, rapid changes in fashion trends, and diverse consumer behavior, especially within e-commerce environments. Traditional forecasting methods such as linear regression and time series models often fall short in addressing the complex dynamics of the modern fashion market. This study presents a novel integration of demand forecasting and size recommendation into a unified AI-based system utilizing the Decision Tree algorithm. The system is designed to predict product demand while also providing personalized clothing size recommendations based on user attributes such as body measurements, style preferences, and seasonal trends. The system was developed using a structured data processing and predictive modeling approach, incorporating user profiles and trend sentiment derived from social media. The evaluation results show that the system achieved an accuracy rate of 87.5% in demand forecasting and 84% user satisfaction for size recommendations. It demonstrated better adaptability and performance compared to traditional methods such as ARIMA. A functional prototype was implemented, allowing users to interactively input data and receive real-time predictions. This study confirms the potential of Decision Tree-based AI models to enhance the shopping experience, reduce product return rates, and optimize inventory management. Future improvements may involve integrating real-time data and advanced technologies such as 3D body scanning to further increase prediction accuracy and personalization in digital fashion retail.
Optimasi Ongkir dan Rute Pengiriman Menggunakan Haversine Formula dan Algoritma Kruskal Adhim, Maulana Fauzil; Minardi, Joko; Saputro, Heru
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2336

Abstract

Optimasi ongkos kirim (ongkir) dan rute pengiriman menjadi tantangan utama bagi usaha mikro, kecil, dan menengah (UMKM), terutama dalam meningkatkan efisiensi logistik. Toko Keripik Aldafa menghadapi permasalahan dalam menentukan ongkir secara akurat serta memilih rute pengiriman yang optimal karena masih menggunakan metode manual. Ketidakakuratan dalam estimasi biaya dan rute yang kurang efisien menyebabkan peningkatan waktu tempuh serta tingginya biaya operasional. Oleh karena itu, penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem berbasis web yang dapat membantu mengoptimalkan ongkir dan rute pengiriman dengan memanfaatkan Haversine Formula untuk perhitungan jarak geografis serta Algoritma Kruskal untuk menentukan jalur pengiriman terpendek. Metode yang digunakan dalam penelitian ini meliputi penerapan Haversine Formula untuk menghitung jarak berdasarkan koordinat lintang dan bujur, serta Algoritma Kruskal dalam membangun Minimum Spanning Tree (MST) guna menemukan jalur distribusi yang paling optimal. Pengujian dilakukan dengan membandingkan hasil perhitungan manual dan sistem berbasis web. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan akurasi estimasi ongkir dan mengoptimalkan rute pengiriman. Dibandingkan dengan metode manual, sistem ini berhasil mengurangi biaya logistik rata-rata sebesar 18,5% serta menghemat waktu tempuh hingga 22,3%. Selain itu, perbedaan hasil perhitungan manual (8.15 km) dan sistem (8.12 km) menunjukkan tingkat akurasi yang tinggi dengan selisih yang kecil. Dengan implementasi sistem ini, UMKM seperti Toko Keripik Aldafa dapat mengurangi ketidakefisienan dalam proses logistik dan meningkatkan daya saing di era digital. Penelitian ini juga membuka peluang pengembangan lebih lanjut, seperti penggunaan data real-time dan integrasi dengan sistem pemetaan lainnya untuk meningkatkan akurasi dan efisiensi distribusi barang.
The Concept of Justice in AI-Driven Legal Decision Making Princes, Elfindah; Rasji, Rasji; Michael
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2338

Abstract

The integration of Artificial Intelligence (AI) into legal decision-making processes has introduced significant advancements in efficiency and predictive capability. However, its implications for justice—particularly fairness, impartiality, transparency, and due process—remain critically debated. This study employs a Systematic Literature Review (SLR) methodology to examine how AI-driven legal decision-making aligns with classical and contemporary philosophical concepts of justice. Drawing on 48 peer-reviewed articles, policy documents, and case studies published between 2015 and 2024, the research identifies four core thematic issues: the persistence of algorithmic bias, the lack of transparency in AI systems, inconsistencies in global regulatory frameworks, and the misalignment of AI logic with moral reasoning. While AI offers promising tools for streamlining judicial processes, its application often risks reinforcing existing inequities and undermining legal principles such as corrective justice and procedural fairness. The study concludes with targeted recommendations for the development of transparent, accountable, and ethically governed AI systems that support—rather than supplant—human judicial discretion. This research contributes to the growing discourse on legal AI by highlighting the necessity of embedding justice-oriented values at the core of technological innovation in the legal sector. This research has several limitations: not based on empirical findings and no validations from experts both in AI and in legal theories. Future research should address these limitations.
Development of The Software as Services (SaaS) Business Model in The Satusehat Integrated Electronic Medical Record System Novantara, Panji; Trisudarmo, Ragel; Fauziah
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2354

Abstract

Digital transformation in the healthcare sector represents a key strategy to enhance operational efficiency and improve the quality of medical services. This study presents the development of a Software as a Service (SaaS)-based Electronic Medical Record (EMR) information system, integrated with SatuSehat, a national health data platform managed by the Ministry of Health of the Republic of Indonesia. The system is designed to improve the accuracy of clinical data recording and expedite access to patient information for healthcare professionals. The development process adopted the Agile methodology, characterized by iterative and incremental stages including requirements analysis, system design, implementation, testing, and evaluation. Agile was selected for its ability to accommodate dynamic user needs and regulatory requirements through continuous feedback loops and adaptive planning. Compliance with national health regulations and data security standards, including Minister of Health Regulation No. 24 of 2022 concerning EMR implementation, guided the entire process. Evaluation of the system demonstrates enhanced efficiency in medical administrative workflows, improved accuracy of patient records, and accelerated clinical decision-making processes. The integration with SatuSehat enables interoperability at a national level, thereby supporting real-time health data exchange and long-term health monitoring systems. From a societal standpoint, the system improves data accessibility for healthcare personnel and elevates the overall quality of care delivered to patients. Economically, the SaaS-based approach reduces operational costs, promotes efficient budgeting, and contributes to the broader digital transformation of healthcare services, particularly in strengthening primary care infrastructure.
Multinomial Logistic Application on Factors Affecting Poor Population in East Java Isyanto, Aisyah Kirana Putri; Trimono; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2400

Abstract

Poverty is still one of the major problems in East Java, even though this province has an important role in supporting the national economy. This condition shows that development in each district/city has not been evenly distributed, so a data-based analysis is needed to determine the factors that influence the poverty rate. This study aims to analyze the influence of socioeconomic variables on the poverty rate category in East Java using a multinomial logistic regression model. The data used is secondary data from the Central Bureau of Statistics (BPS) in 2023 which covers 38 districts/cities. The independent variables analyzed consisted of life expectancy, average years of schooling, open unemployment rate, labor force participation rate, expenditure per capita, human development index, and gross regional domestic product (GRDP) per capita. The analysis process involved data exploration, multicollinearity test, multinomial logistic regression modelling, simultaneous and partial parameter significance test, and model performance evaluation. The results show that per capita expenditure is the only variable that has a significant effect on poverty level classification. The model is able to classify the data with an accuracy of 81% and a McFadden R² value of 0.6483, which means the model has a fairly good performance. This finding shows the importance of increasing people's purchasing power as an effort to reduce poverty. This research is expected to be a reference for local governments in formulating more targeted and data-based policies.            
Evaluation of Watsons ID Application Adoption Among Generation Z Users: Applying the UTAUT2 Sitompul, Byanca Rebecca Yocelyn; Wulansari, Anita; Safitri, Eristya Maya
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2405

Abstract

The rapid advancement of digital technology has significantly transformed the retail landscape, particularly within the beauty and health e-commerce sector. In response to this shift, the Watsons ID application was developed to provide a fast, convenient, and feature-rich platform for online shopping. This study examines how Generation Z consumers in Indonesia adopt and use the Watsons ID application by applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM), with data collected from 410 respondents through purposive sampling. The research specifically targeted Indonesian Generation Z users who have experience with the Watsons ID application. The findings indicate that Performance Expectancy, Social Influence, Price Value, and Habit have a significant positive impact on users' intention to use the application. Furthermore, Habit and Behavioral Intention positively influence actual usage behavior. Conversely, Effort Expectancy and Facilitating Conditions do not show a significant effect on Behavioral Intention, and Facilitating Conditions also do not affect Use Behavior. These insights provide valuable guidance for e-commerce developers and beauty retailers seeking to optimize digital strategies, enhance user experience, and strengthen customer engagement specifically targeted toward Generation Z consumers in the Indonesian market.
Optimizing 3-Axis CNC Router Design: Using QFD and DFM for Enhanced Precision and Efficiency Firgiawan, Mohammad Rivo; Nurcahyanie, Yunia Dwie
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2407

Abstract

The development of the manufacturing industry demands continuous innovation in machine technology, particularly the 3 Axis CNC Router, which plays a crucial role in various applications such as cutting, milling, and material shaping.This research aims to design and develop a more efficient and precise 3 Axis CNC Router machine with a user needs-based approach in Indonesia.This machine is optimized using Quality Function Deployment (QFD) to identify consumer needs through questionnaire data, as well as Design for Manufacture (DFM) to ensure a design that is easy to produce and cost-efficient.The results of the QFD and DFM analysis are used to develop the machine with superior features, such as improved cutting precision, reduced noise, and enhanced resistance to corrosion and material dust accumulation. The developed machine is capable of improving cutting accuracy to ±0.05 mm and processing speed by 20% faster compared to the previous machine.Additionally, the noise reduction also reaches more than 15 dB, enhancing operator comfort during use.This research also identifies the main customer needs, such as machine safety, speed, and machine strength, as well as secondary needs, such as aesthetics and dirt prevention.These findings not only provide technical solutions to the existing issues with CNC machines but also suggest a direction for developing machines that are more responsive to the needs of small and medium-sized enterprises in Indonesia, as well as more efficient in material and cost usage.

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