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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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Analisis Sentimen Analisis Sentimen Publik Terhadap Pariwisata Aceh di Media Sosial X Menggunakan Algoritma Naive Bayes Classifier Yahya, Susilawati; Wahyuni , Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1700

Abstract

Aceh has been synonymous with negative perceptions among people outside the province. This is due to the prolonged armed conflict and the devastating tsunami in 2004. Despite these challenges, Aceh possesses abundant potential for tourism, including natural attractions, historical sites, cultural arts, and religious tourism. However, negative perceptions continue to influence tourists' decisions to visit Aceh. Therefore, this study aims to analyze public sentiment or public opinion towards Aceh's tourism using the Naive Bayes algorithm on the X (Twitter) social media platform. Data for this study was collected from tweets on X (Twitter) using the keyword "Aceh tourism" and then underwent several data pre-processing stages to improve data quality, including text cleaning, case folding, word normalization, tokenization, stop word removal, and stemming. Afterward, the Naive Bayes algorithm was applied to classify tweet sentiment into positive and negative categories. Model evaluation was conducted using a confusion matrix, accuracy, and classification report. The results showed that Naive Bayes performed well in classifying public sentiment with an accuracy of 81%. This analysis indicates that public perception towards Aceh's tourism has begun to shift positively, presenting a promising opportunity for the future development of Aceh's tourism sector.
Analisis Data Mining Dalam Pemilihan Smartphone dan Klasifikasi di Berbagai Perangkat Menggunakan Random Forest Aulia, Ananda; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1703

Abstract

Abstract− Smartphone technology continues to develop rapidly, driving the need for effective analysis methods to assist users in selecting devices that suit their needs. This research aims to implement data mining using the Random Forest method in the process of selecting smartphones and classifying devices based on their technical specifications. The Random Forest method was chosen because of its reliable ability to handle data with a large number of attributes, produce an accurate classification model, and minimize the risk of overfitting. The dataset used includes technical specifications of various smartphones, such as camera resolution, chipset, RAM capacity, screen resolution, and support for 4K video recording. The research process involved data collection, pre-processing to handle missing values ​​and data transformation, as well as model training using the Random Forest algorithm.  The research results show that the Random Forest method is able to classify devices with high accuracy, helping users determine smartphones that meet their criteria, such as support for 4K video recording and overall performance. Additionally, this research provides insight into the importance of certain attributes in smartphone selection. Thus, implementing data mining using Random Forest can be an effective solution in supporting data-based decision making in the field of consumer technology. Keywords: Data Mining, Random Forest, Smartphone, Classification, Technical Specifications
Optimasi Strategi Penjualan Am2000 Tirtamart Dengan Algoritma Apriori Untuk Mengidentifikasi Produk Favorit Pelanggan Sugito, Bambang; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1707

Abstract

The retail industry faces increasing competition, and to survive, companies need to understand customer purchasing behavior and optimize their sales strategies. One effective approach is the use of data mining to analyze sales data and identify purchasing patterns. This study aims to optimize the sales strategy of Toko AM2000 by applying the Apriori algorithm to identify the most popular products among customers. The data used includes sales transactions from January to September 2024, with a total of 1,000 transactions and 10 attributes. The results of the analysis using the Apriori algorithm show a significant association between the products "Water Softener" and "Filter Tank," although the support value obtained, which is 20.4%, does not meet the minimum support threshold of 30%. However, the confidence value of 80.6% indicates a high likelihood that customers who purchase "Water Softener" also buy "Filter Tank." This suggests that Toko AM2000 should focus its marketing strategies on promoting these two products. To improve the effectiveness of the analysis, it is recommended to lower the minimum support value, increase the number of transactions, and consider using other algorithms, such as K-means. This study provides valuable insights for business decision-making and the enhancement of Toko AM2000's marketing strategy.
Analysis Of Public Sentiment Towards The Corruption Eradication Commission On Twitter Nurhaliza Sofyan, Siti; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1711

Abstract

The Corruption Eradication Commission (KPK) is a state institution in Indonesia which was formed to eradicate corruption. The Corruption Eradication Committee (KPK) [1]has the main task of carrying out investigations, inquiries and prosecutions of criminal acts of corruption. This institution is independent and free from the influence of any power in carrying out its duties and authority [2]. This research explores the analysis of Indonesian people's sentiment towards the KPK in the current situation such as arrests for corruption and the policies and actions carried out by the KPK. Sentiment analysis used in the journal with data obtained from Twitter data and using Orange Data Mining, with multilingual sentiment analysis techniques to analyze Indonesian people's sentiment towards the KPK agency. The results of sentiment analysis are visualized through box plots and scatter plots, which aim to classify Twitter users based on their emotional responses. The findings of this research provide valuable insight into the landscape of sentiment surrounding the Corruption Eradication Commission's bicycles, as well as providing sustainable benefits and are expected to be used as material for evaluating the government's role. Data totaling 300 tweets were processed using text mining techniques in the Orange Data Mining application [3][4]. This technique consists of several stages of text processing, namely transformation, filtering, and tokenization. The text processing results are extracted via wordcloud to find out the features of words that are often discussed by the public. After that, sentiment analysis was carried out to determine public opinion regarding the KPK institution based on positive, negative and neutral categories [5], [6]
Penerapan Algoritma Apriori untuk Optimasi Strategi Penjualan Berdasarkan Analisis Pola Pembelian di Torsa Cafe Ibezato Zalukhu, Anzas; Sartika, Dewi; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1715

Abstract

This study aims to analyze consumer purchasing patterns at Torsa Café using data mining methods with the Apriori algorithm to discover association rules between products that are frequently purchased together. In facing the increasingly competitive business environment in the food and beverage industry, understanding consumer purchasing behavior becomes key to enhancing marketing and operational strategies. This research uses sales transaction data from October 2024, consisting of 31 transactions with a total of 129 items. The analysis process begins with data collection and normalization of transaction data, followed by the application of the Apriori algorithm to calculate the support and confidence values of items in the transactions. The analysis results show several items with high support levels, such as "Sanger Espresso", "Avocado Cappuccino Torsa", and "Kopi Susu Torsa", with support values above 30%. Additionally, product combinations frequently purchased together, such as Kopi Tancap with Redvelvet, Macchiato, Frappucino, and Kopi Susu Torsa, can serve as the basis for promotions or more efficient stock management. These findings provide valuable insights for Torsa Café management to determine product placement strategies, raw material stock management, and design more targeted promotions based on the identified purchasing patterns. Therefore, the results of this study are expected to improve operational efficiency and enhance Torsa Café’s competitiveness in the increasingly competitive market.
Analisa data Untuk Menentukan Kelulusan Proposal Penelitian Dosen internal STMIK Triguna Dharma Menggunakan Metode Analytical Hierarchy Proses (AHP) Ayu, Ayu Ofta Sari; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1726

Abstract

he selection process for lecturer research proposals at STMIK Triguna Dharma is often faced with challenges in objective and transparent assessment. This is due to the high number of proposals submitted and the variety of criteria that need to be considered, such as scientific contribution, innovation, relevance and implementation potential. This research aims to develop a more objective selection model by applying the Analytical Hierarchy Process (AHP) method in determining the feasibility of lecturer research proposals. The AHP method is used because of its ability to break down complex problems into a hierarchical structure and calculate the priority weights of each assessment criterion. This research process begins with identifying the main criteria and sub-criteria, followed by collecting data from research proposals, expert interviews, and literature studies. Each criterion is evaluated through a pairwise comparison matrix to obtain objective priority weights. Next, all research proposals are assessed based on the weight of the criteria and ranked to determine the most feasible proposal. The research results show that the criteria for scientific contribution and relevance to institutional goals have the highest weight in the assessment, making them the main aspects in the proposal selection process at STMIK Triguna Dharma. With the AHP method, this research concludes that the proposal selection process can be carried out more systematically, transparently and fairly. The implementation of this model is expected to be able to help institutions fund research that is most relevant and has a significant impact in accordance with the campus' vision and mission. Keywords: Lecturer research; Analytical Hierarchy Process; Data analysis; Matrix; Ranking
Implementasi Data Mining Dalam Mengelompokkan Tingkat Kepuasan Pemakaian Jasa Cleaning Service Dengan Menggunakan Algoritma K-Means Clustering Nadya, Nadya Septiani; Wahyuni, Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1729

Abstract

Pinang Jaya Abadi Indonesia is a company providing cleaning services to various sectors, including hospitals, commercial businesses, offices, and shopping centers. However, problems arise when complaints regarding the quality of service provided by its employees occur. To improve service quality and assess customer satisfaction with the offered services, a system capable of accurately and efficiently clustering customer satisfaction data is needed. As a solution, this study applies the K-Means Clustering algorithm in the field of Data Mining to cluster customer satisfaction data regarding the cleaning services provided by PT. Pinang Jaya Abadi Indonesia. The K-Means algorithm was chosen for its ability to cluster data quickly and effectively, and its proven efficiency in various data clustering cases. By using this algorithm, the study aims to produce more structured and informative data clusters, providing a clearer understanding of customer satisfaction levels. The results of this study show that the system designed using the K-Means Clustering algorithm can effectively cluster customer satisfaction data, yielding efficient and accurate results. This system can serve as a tool for PT. Pinang Jaya Abadi Indonesia to enhance service quality and minimize customer complaints by focusing more on clusters with low satisfaction levels.
Automatic Detection of Diabetic Retinopathy Eye Fundus Images Using Matlab Siska, Siska Atmawan Oktavia
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1742

Abstract

Diabetic Retinopathy (DR) is one of the causes of diabetes mellitus and is an important cause of visual disability and blindness. Screening of diabetic retinopathy is essential for both early detection and early treatment. Currently, the ophthalmologists use a non-mydriatic fundus camera to capture retinal images. Based on the fundus images, the ophthalmologists diagnose manually, which is time-consuming and prone to errors. The objectives of this project are to study image processing techniques, particularly on fundus images for diabetic retinopathy screening, to develop an automatic screening and classification system for diabetic retinopathy using fundus images in order to detect diabetic retinopathy at an early stage, and finally, to propose use of new eye fundus images, expert diagnosis image processing techniques, machine learning classifiers, and also App Designer as the Graphical User Interface (GUI) environment for early detection of the signs of diabetic retinopathy. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy.
Analisis Tren Pendaftaran Siswa Menggunakan Big Data di Yayasan Pendidikan Raksana Medan Nadya, Nadya Septiani; Iqbal, Muhammad
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1744

Abstract

Yayasan Pendidikan Raksana Medan is an educational institution encompassing SMP, SMA, SMK-1, and SMK-2 levels. With an increasing number of students each year, analyzing student enrollment data has become crucial for strategic planning and decision-making. This study aims to analyze student enrollment trends using a Big Data approach to identify enrollment patterns, study program preferences, and factors influencing the number of applicants. The data used includes enrollment information from the past five years, such as demographic data, program choices, and enrollment timing. The analysis was conducted using data mining methods and data visualization to identify specific trends and patterns. The results of the study indicate a significant increase in applicants to vocational programs, with the majority of applicants coming from areas around Medan. These findings are expected to assist Yayasan Pendidikan Raksana Medan in improving marketing strategies and curriculum adjustments based on student and community demand
A Text Mining Approach to Analyzing the Role of Negative Sentiment Words in News Articles on Suicide and Related Incidents Subagio, Selamat; Samsir, Samsir; Dalimunthe, Abdul Hakim; Ronal Watrianthos
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1745

Abstract

This study examines the role of negative sentiment words in news media coverage of suicide and related incidents through analysis of 1,515 news articles published between 2019 and 2024. Using advanced text mining techniques and sentiment analysis, we investigated patterns in emotional language use and their impact on public discourse. The research revealed frequent usage of negative sentiment words such as "crisis" (256 occurrences), "despair" (214 occurrences), and "death" (189 occurrences), which significantly influenced the emotional framing of these sensitive topics. Statistical analysis showed strong correlations between negative sentiment words and mental health-related terms (correlation value 0.75), indicating consistent patterns in media narrative construction. Temporal analysis identified a notable increase in negative sentiment during the COVID-19 pandemic (2020-2021), followed by a shift toward more solution-focused coverage in 2022-2024. The findings suggest that while negative sentiment words are inherent in covering suicide-related topics, their use can be balanced with solution-oriented language to promote more responsible reporting. This research contributes to understanding how emotional language shapes public discourse on mental health crises and provides insights for developing more effective guidelines for responsible journalism.

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