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Bulletin of Information Technology (BIT)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27220524     DOI : 10.47065/bit.v2i3.106
Core Subject : Science,
Computer Science & IT
Jurnal Bulletin of Information Technology (BIT) memuat tentang artikel hasil penelitian dan kajian konseptual bidang teknik informatika, ilmu komputer dan sistem informasi. Topik utama yang diterbitkan mencakup:berisi kajian ilmiah informatika tentang : Sistem Pendukung Keputusan Sistem Pakar Sistem Informasi, Kriptografi Pemodelan dan Simulasi Jaringan Komputer Komputasi Pengolahan Citra Dan lain-lain (topik lainnya yang berhubungan dengan teknologi informasi)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 256 Documents
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Optimizing CCTV Damage Diagnosis with Backward Chaining Based Expert System Lasena, Marlin; Rahayu Ningsi Ahmad, Sulistiawati
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1964

Abstract

The use of CCTV systems in daily life is increasingly widespread, along with the growing need for security and surveillance. However, malfunctions in both hardware and software components of CCTV devices remain a challenge, especially for technicians and non-technical users who lack sufficient expertise. This study aims to develop an expert system using the Backward Chaining method to assist technicians and users in accurately and efficiently diagnosing various types of CCTV malfunctions. The Backward Chaining method is employed due to its ability to trace symptoms back to the root cause using a rule-based logical inference approach. The system is implemented as a mobile application for Android platforms, with a knowledge base constructed from the expertise of CCTV technicians at PT. Smart CCTV Indonesia. The results of the study indicate that the expert system provides significant ease in diagnosing CCTV issues and offers relevant recommendations to both technicians and users. Thus, this system is expected to enhance efficiency in troubleshooting processes and support better decision-making in the management of digital security systems
Prediksi Harga Tiket Pesawat Domestik Rute Perjalanan Surabaya-Jakarta Menggunakan Metode Regresi Linear Berganda Assara, Enggi Sabrilla; Setiawan, Hamzah; Suprianto
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1975

Abstract

Air transportation is highly favored for its time efficiency and comfort, especially on busy routes such as Surabaya–Jakarta. However, the dynamic fluctuation of airline ticket prices often makes it difficult for consumers to plan their trips. This study aims to develop a predictive model for airline ticket prices on the Surabaya–Jakarta route using the multiple linear regression method. A total of 10,000 rows of data were analyzed using statistical approaches and analytical processes based on Google Colaboratory, involving stages such as data import, preprocessing, variable transformation, data splitting (training and testing), and classical assumption testing. The resulting regression model demonstrated excellent performance with an R-squared value of 96.4%, indicating that most of the price variation could be explained by independent variables such as airline, departure time, travel duration, baggage capacity, and service type. Violations of assumptions such as normality and heteroskedasticity were addressed through logarithmic transformation and the use of regression with robust standard errors. Furthermore, multicollinearity was minimized using Ridge Regression. Model evaluation showed no signs of overfitting and produced stable prediction results. Only a few variables were statistically significant, highlighting the importance of analyzing variable contributions to enhance model efficiency. The predictive model developed in this study provides accurate and practical results, making it useful for consumers in travel planning and for airlines in developing more competitive pricing strategies.
Penerapan Data Mining Untuk Prediksi Penjualan Roti Terlaris Menggunakan Metode K-Nearest Neighbor Arifin Munthe; Kusmanto; Basyarul Ulya
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1978

Abstract

Penelitian ini dilakukan untuk memprediksi penjualan roti terlaris menggunakan metode K-Nearest Neighbor (KNN) pada Pabrik Roti SAMAWA Rantauprapat. Dengan memanfaatkan teknik data mining, penelitian ini bertujuan memberikan informasi yang akurat bagi pengambilan keputusan penjualan. Data mining merupakan proses untuk menemukan pola tersembunyi dalam data, sedangkan metode KNN adalah algoritma klasifikasi yang bekerja berdasarkan kedekatan jarak antar data. Model klasifikasi ini efektif untuk menganalisis data numerik dan memetakan data baru berdasarkan data historis. Penelitian ini menggunakan pendekatan kuantitatif dengan tahapan pengumpulan data, preprocessing, pembagian data training dan testing, serta evaluasi model menggunakan aplikasi Orange. Data yang digunakan sebanyak 150 data penjualan, dan analisis dilakukan dengan menggunakan algoritma KNN untuk proses klasifikasi. Hasil klasifikasi menunjukkan bahwa dari 100 data testing, sebanyak 59 diklasifikasikan sebagai "laris" dan 41 sebagai "tidak laris". Evaluasi model menghasilkan nilai akurasi sebesar 85% dan AUC sebesar 0,948, yang menunjukkan performa model cukup tinggi. Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode KNN efektif digunakan dalam memprediksi penjualan roti terlaris. Model ini diharapkan dapat membantu pihak pabrik dalam mengelola stok dan strategi pemasaran dengan lebih baik.
The Analisa Pengaruh Kepuasan Pengguna Website Talipodo Golden Theatre Menggunakan Metode Webqual 4.0 Reka Ainul Khasanah; Sucipto; Nugroho, Arie
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1987

Abstract

This study aims to assess the quality of the Golden Theatre website based on user satisfaction using the WebQual 4.0 method. The research focuses on three main dimensions: usability, information quality, and service interaction quality. A quantitative descriptive approach was employed through a survey of 100 active users of the Golden Theatre website. Data were analyzed using Prais-Winsten regression and classical assumption tests, processed with Python programming language. The results indicate that among the three variables, only information quality has a significant influence on user satisfaction. Meanwhile, usability and service interaction quality do not have a significant partial effect. Simultaneously, the three variables have a significant impact on user satisfaction. The WebQual Index (WQI) score of 57.19% suggests that the website’s overall quality is moderate but still falls short of user expectations. Therefore, it is recommended that the Golden Theatre website management enhance the quality of information provided, in order to improve service quality and overall user satisfaction.
Analisis Sentimen Terhadap Kontroversi Pembangunan IKN Di Media Sosial Twitter Menggunakan Metode Naive Bayes Dimas, Muhammad; Drs. Azahari; Muhammad Ibnu Sa’ad
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1993

Abstract

The relocation of Indonesia's capital city (IKN) from Jakarta to East Kalimantan is a national strategic policy that has generated diverse public responses. On one hand, it is seen as an effort to promote equitable development, but on the other hand, it has drawn criticism related to its environmental, social, and financial impacts. Social media, particularly Twitter, has become a popular platform for expressing public opinion on this issue. This study aims to analyze public sentiment toward the IKN development as expressed through Twitter posts. By understanding public sentiment trends, this research seeks to provide insights into public perception that may serve as valuable input for government evaluation and policymaking. The research employed a quantitative approach using data mining techniques. Data were collected through web crawling using the snscrape library and underwent several pre-processing stages, including cleansing, case folding, tokenization, stopword removal, and stemming. Sentiment analysis was conducted using a lexicon-based approach, combined with a Naïve Bayes classification algorithm supported by TF-IDF weighting. Based on 2,178 analyzed tweets, the results showed that positive sentiment dominated at 52.4%, followed by negative sentiment at 28.4%, and neutral sentiment at 19.3%. The classification model achieved an accuracy rate of 75.69%. These findings indicate a general tendency of public support for the IKN development and highlight the importance of sentiment analysis as a strategic tool for interpreting public opinion in the digital era
Penerapan Algoritma K-Means Clustering pada Kinerja Mesin Screw press Kurnia Rahman, Fikri; Jasril; Sanjaya, Suwanto; Handayani, Lestari; Insani, Fitri
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2002

Abstract

The screw press is one of the machines used in the process of separating oil from tanks containing Fresh Fruit Bunches (FFB). The machine consists of a twin-screw system that functions to extract oil from the pressing unit, with back pressure applied by a hydraulic double cone. The mixed fruit residue is compreWCSSd, causing the oil contained within the residue to be released due to the pressure exerted by the press machine. Maintenance and repair of machinery are eWCSSntial activities to support productive operations in any sector. Therefore, it is necessary to conduct analysis to identify patterns in machine conditions within the factory. One effective approach to discovering machine condition patterns is through clustering techniques. Clustering is a method of grouping data based on certain parameters to form clusters of objects that share similar characteristics. In this study, data were collected from PT. XYZ for the period of April 2024 to May 2024, with a total of 23,002 records. The analysis was conducted using the K-Means Clustering algorithm, with testing carried out on 3 to 15 clusters. Based on the evaluation using the Davies-Bouldin Index (DBI), the most optimal clustering result was obtained with 3 clusters, achieving the lowest DBI value of 0.386. Meanwhile, using the Elbow Method, the optimal number of clusters was determined to be 4, as indicated by the Elbow point on the WCSS graph, with a Sum of Square Error (WCSS) value of 270. Therefore, it can be concluded that the clustering results using the K-Means Clustering algorithm are relevant for identifying machine condition patterns and are expected to assist in monitoring and managing the condition of the screw press machine.
Implementasi Model Long Short Term Memory (LSTM) dalam Prediksi Harga Saham Kurniansyah, Juliandi; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2005

Abstract

Stock market investment is gaining popularity, although predicting stock price fluctuations remains challenging. Accurate stock prediction models can assist investors in decision-making. In this research, a Long Short-Term Memory (LSTM) model was employed to make predictions regarding the stock prices of BBCA based on daily historical data from January 1 2015 to January 1 2025. The data was gathered from the Yahoo Finance website, utilizing only the closing price ('close') variable. The research process included data pre-processing, Min-Max normalization, LSTM modeling with varying timesteps (30, 60, 90 days), and evaluation of prediction results. The LSTM model was built with two LSTM layers, a dropout layer, and a final dense layer, and its training involved the application of the mean_squared_error loss function and Adam optimizer. Evaluation results showed that the model configuration with 60 timesteps achieved optimal performance with a RMSE of 114.17, MAPE percentage of 0.96%, and an R-Squared of 0.98, indicating highly accurate and reliable predictions. This study demonstrated that LSTM is an effective model for stock price prediction based on time series data.
Optimasi Pemilihan Lokasi Usaha Menggunakan Sistem Pendukung Keputusan Berbasis AHP Gunawan Sudarsono, Bernadus; Galih Whendasmoro, Raditya
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2007

Abstract

In the midst of increasingly intense business competition and constantly changing market dynamics, the ability to make the right strategic decisions is crucial for an organization's success. Decision-making that involves various criteria often faces high complexity and subjectivity, requiring methods that can manage and simplify this process. Decision Support System (DSS) is a system designed to assist decision-makers in dealing with semi-structured or unstructured situations. This research aims to develop a decision support system based on the Analytic Hierarchy Process (AHP) that can help entrepreneurs determine the optimal business location. The Analytic Hierarchy Process (AHP) is a decision-making method developed by Thomas L. Saaty in the 1980s. A normalization matrix is used to determine the weight of each alternative. The aggregation of weights shows that alternative A3 has the highest rank, followed by alternative A2. Therefore, alternative A3 can be considered the best choice based on the predefined criteria. The AHP method helps in making more informed and objective strategic decisions by integrating various criteria and preferences, thus providing a comprehensive view in selecting the best alternative.
Evaluasi Kinerja Pegawai Berbasis Teknologi: Implementasi Sistem Pendukung Keputusan dengan Metode Profile Matching Suhada, Karya; Hendrik, Dede; Andriyana, Andriyana; Isnandar, Evi; Yanitasari, Yessy
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2019

Abstract

Employee performance evaluation is an important process to improve work efficiency and effectiveness in government agencies. At the Simpang Empat District Office, the performance evaluation process is still carried out manually, which is often time consuming and prone to subjectivity. This research aims to develop a Decision Support System (DSS) based on the Profile Matching method to assist the employee performance evaluation process. This research uses the Profile Matching method, where the ideal performance profile is used as a reference for assessing individual performance. Employee performance data is collected through work assessments, interviews and direct observation. The SPK system developed is designed to automate the evaluation process by comparing employee performance profiles with ideal performance profiles. This system succeeded in reducing evaluation time by up to 50% and increasing assessment accuracy compared to manual methods. Employees with substandard performance can be identified quickly, allowing for timely intervention.
Rekomendasi Sparepart Pada Bengkel Robbi Motor Berbasis Algoritma Apriori Suharni; Putri Husain, Nursuci; Atsari Hardiman, Ashriyanto
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2024

Abstract

The development of transportation, especially two-wheeled motorized vehicles, drives the increasing demand for maintenance services and the availability of spare parts. However, many workshops still face challenges in managing spare parts stock, which is handled manually. This study aims to design and develop a spare parts recommendation system at Robbi Motor Workshop using the Apriori Algorithm, as well as to test the performance of the developed system. The method used is data mining with association techniques, where the Apriori Algorithm is applied to discover spare parts purchasing patterns from transaction data. The system enables users to analyze transactions based on a selected time range without the need to manually input minimum support and confidence values. The results show that the system is capable of generating relevant association rules, such as: “If consumers buy Engine Oil, then consumers will also buy Axle Oil”, with a support value of 67% and a confidence value of 86%. In addition, the system’s accuracy was tested using the lift value against two recommendation rules: (1) Engine Oil → Axle Oil with a lift value of 0.9949, and (2) Inner Tire → Axle Oil with a lift value of 1.0714. A lift value > 1 indicates that the combination of items has a stronger association than random occurrence. The system is implemented as a web-based application using the Laravel framework, equipped with features for transaction data management, Apriori analysis, analysis history, and exporting analysis results to PDF format. Testing using the blackbox method shows that the system operates according to specifications and produces accurate outputs. With this recommendation system, it is expected that the workshop can improve the efficiency of spare parts stock management.

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