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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Biogenerasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Multidisciplinary Applied Natural Science Bulletin of Information Technology (BIT) EXPLORER Jurnal Pengabdian Masyarakat Gemilang (JPMG) Journal of Scientific Research, Education, and Technology Buletin Ilmiah Informatika Teknologi Prosiding Seminar Nasional Unimus International Conferences On Early Childhood Education Proceedings
Andi Ernawati
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Journal : Bulletin of Information Technology (BIT)

 1 
Implementasi Sistem Pendukung Keputusan dalam menentukan Kecamatan Terbaik Menggunakan Algoritma Entropy dan Additive Ratio Assessment (ARAS) Ernawati, Andi; Ofta Sari, Ayu; Sofyan, Siti Nurhaliza; Aulia, Ananda; Sitorus, Zulham; Khairul
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1066

Abstract

In the context of regional development and decision making related to determining the best village, the use of a Decision Support System (DSS) with the application of the Entropy and Additive Ratio Assessment (ARAS) algorithms is a very important approach. The main objective of this research is to propose and implement a method that utilizes the Entropy algorithm to evaluate criteria weights and ARAS to rank villages based on predetermined criteria. This approach begins the process by identifying relevant criteria to determine the best village in an area. Next, the Entropy algorithm is used to measure the level of importance or relative weight of each predetermined criterion. This step helps in assessing how informative each criterion is in the decision-making process regarding determining the best Village. After determining the criteria weights using Entropy, the approach continues with the application of the ARAS method. ARAS is used to rank villages based on normalized values ​​from previously determined criteria. The data normalization process is carried out to ensure the validity of comparisons between villages. The final result of this approach is a ranking of villages indicating the best villages based on the criteria considered. This method was tested in a case study using a dataset involving a number of relevant criteria for assessing village development potential. Experimental results show that the use of the Entropy and ARAS algorithms in the Decision Support System provides an effective and informative framework for decision makers in determining the best Village. In conclusion, this approach provides a solid foundation to support a more effective and precise decision-making process in regional development based on clearly defined criteria.
Implementasi Algoritma Naïve Bayes dalam Menganalisis Sentimen Review Pengguna Tokopedia pada Produk Kesehatan Ernawati, Andi; Sari, Ayu Ofta; Sofyan, Siti Nurhaliza; Iqbal, Muhammad; Wijaya, Rian Farta Wijaya
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1090

Abstract

It must be realized that customer satisfaction is the main goal for companies in developing their business. Because customers' opinions written on social media will have a big influence on the company and potential customers. In its development, it is increasingly found in various online media, one of which is Tokopedia. Product reviews are an important source of information regarding quality, service and delivery from both consumers and manufacturers. With a very large amount of data for each product on Tokopedia, analyzing and concluding product review information will definitely take a lot of time if done manually. To overcome this, a sentiment analysis system is needed that can automatically extract important information that can objectively determine product quality and handle large amounts of textual information. The sentiment analysis system consists of several stages, namely crawling, pre-processing, word weighting, and sentiment classification. By applying the Naïve Bayes algorithm through selecting range and frequency features, accuracy, accuracy and recall results will be obtained using the Confusion Matrix test. The dataset used is from the kaggle.com site regarding customer sentiment on health products with the type of mask. using the Naïve Bayes Algorithm Method to determine the sentiment of user reviews by classifying 2 positive and negative classes using the NLP approach produces an accuracy value of 88%.
Application of the C45 Algorithm to Predict Student Academic Scores Ernawati, Andi; Sitorus, Zulham; Aulia, Ananda; Ayu Ofta
Bulletin of Information Technology (BIT) Vol 5 No 2 (2024): Juni 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i2.1251

Abstract

Student grades are the results of teaching and learning activities on a campus. So you can know your target for completing your studies. This research uses the C4.5 Algorithm which can help predict the results of student assessments. The dataset consists of student achievement index, place of residence, discipline, lecturer's role in lectures. From 40 datasets we have obtained a decision on student academic achievement and obtained performance results from accuracy results of 86.36% with class precision predicate Yes=84.62%, No=88.89% and class recall Yes=91.67%, No=80.00%.
Implementation of E-Commerce System as SME Development Strategy in the Digital Era Saputra, Maulian; Susilawati; Nurhaliza Sofyan, Siti; Aulia, Ananda; Ernawati, Andi; Oftasari, Ayu; Farta wijaya, Rian
Bulletin of Information Technology (BIT) Vol 5 No 3: September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i3.1545

Abstract

The implementation of e-commerce systems has become one of the main strategies in the development of Small and Medium Enterprises (SMEs) in the digital era. E-commerce allows SMEs to expand market reach, improve operational efficiency, and strengthen relationships with consumers through better data access. In addition, this digital platform offers benefits such as distribution cost savings, business process automation, and improved customer service. However, challenges in e-commerce adoption for SMEs include limited digital literacy, uneven technology infrastructure, and cybersecurity issues. To achieve the full potential of e-commerce, support from the government and private sector in the form of adequate policies, infrastructure, and training is required. This research aims to identify the benefits, challenges and solutions in implementing e-commerce for SMEs, in order to improve their competitiveness in an increasingly competitive global market.
Analisis Data Mining Pola Penggunaan Seluler dan Klasifikasi Perilaku Pengguna di Berbagai Perangkat Menggunakan Metode C4.5 Ernawati, Andi; 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.v5i4.1689

Abstract

Along with the development of digital technology, the use of mobile devices is increasing rapidly and affects user behaviour in accessing information and interacting with digital applications. This research aims to analyse mobile device usage patterns and classify user behaviour across various devices by utilising the C4.5 data mining method. The data used in this study was obtained from the Kaggle.com platform which provides a dataset of mobile device usage patterns, including variables such as frequency of application use, duration of device use, and type of application accessed. The research stages include data collection, data pre-processing to ensure quality, and analysis using the C4.5 algorithm. The C4.5 algorithm was chosen due to its ability to build a decision tree model that can classify user behaviour with a good level of accuracy. The results of this study show that there are certain patterns in mobile device usage that can be linked to demographic characteristics and user preferences for device types and applications. The resulting decision tree model is able to classify user behaviour with an accuracy rate of 41.71%%, and shows that social media applications and streaming applications are the most frequently used categories on mobile devices. This research is expected to provide insights for app developers and digital marketers in understanding user behaviour and optimising mobile-based interaction strategies. In addition, the results of this study also contribute to the application of the C4.5 method for analysing mobile technology usage patterns in the context of big data. Keywords: Data Mining, C4.5, Mobile Usage Pattern, User Behaviour Classification,Rapidminer Decision Tree...
Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors Ernawati, Andi; Khairul; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli
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.v6i1.1783

Abstract

This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.
Penentuan Bibit Kelapa Sawit Unggul Dengan Metode ARAS Dan TOPSIS Nur Alam, Sitti; Yesputra, Rolly; Zikra Syah, Arridha; Parini; Ernawati, Andi
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2213

Abstract

The Industrial Era 4.0 opens up great opportunities to increase production, efficiency and sustainability of the palm oil industry. The problem faced by farmers is that farmers are often hampered by limited knowledge and lack of guidance in choosing plant seeds. Because seeds are an important factor in supporting satisfactory results. This research was carried out to help farmers who have difficulty in choosing oil palm seeds which could become a problem for farmers in the future. day. This research uses the ARAS and TOPSIS methods to evaluate seeds based on criteria that have been identified and analyzed, to assess 10 types of superior seeds based on 5 criteria: oil potential, pest resistance, seed price, productive planting period, and maintenance costs. It is hoped that this research can help oil palm farmers increase their productivity and profits, as well as support the sustainability of the palm oil industry in the Industry 4.0 era. The ARAS and TOPSIS methods have proven to be effective in helping farmers choose superior oil palm seeds. From the results of research conducted using the ARAS and TOPSIS methods, VIM 1 seeds were recommended as the best choice based on the points obtained.
Co-Authors - Idris Abdul Karim Ahmad Johari B. Sihes Alam, Sitti Nur Aris Tri Wahyudi Arridha Zikra Syah Aulia, Ananda Ayu Ofta Buton Buton Cucum Sumiati Darmeli Nasution Durrotul Muniroh Ahdaniyah Farta wijaya, Rian Idris Idris Iman Rusmana Iwan Purnama Khairul Mia sumiani Madi Misnarliah Mohd. Anuar B. Abdul Rahman Muhammad Arif Mansur Muhammad Iqbal Nurhaliza Sofyan, Siti Ofta Sari, Ayu Oftasari, Ayu Parini Puridawaty , Brigita Rolly Yesputra Salki Sasmita Saputra, Maulian Sari, Ayu Ofta Siregar, Andree Risky Yuliansyah Sitorus, Zulham Sofyan, Siti Nurhaliza Sri Budiarti Poerwanto sri murtini . Sri Wahyuni Sugeng Santoso Susilawati Syahruddin Lasadike Wijaya, Rian Farta Wijaya, Rian Farta Wijaya