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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Budimas : Jurnal Pengabdian Masyarakat Science Technology and Management Journal (STMJ)
Lutfina, Erba
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences Other

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Journal : Science Technology and Management Journal (STMJ)

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Identifikasi Teknik Data Mining Metode Asosiasi: Systematic Literature Review Lutfina, Erba; Yuanita, Syafira Putri; Dimentieva, Imelda; Pribadi, Ahmad Naufal; Syahidan, Mukhammad Shaunan; Safa’at, Mochammad Alief Fauzan Akbar; Loekito, Joseph
Science Technology and Management Journal Vol. 4 No. 2 (2024): Agustus 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v4i2.257

Abstract

Data mining is a process of collecting important information from large data. Data mining has many functions, one of which is the description function. In this case, the description function is that later data mining can find certain patterns hidden in data. This research aims to identify which association methods in data mining are most often used, identify the most dominant dataset models in association methods, identify the most relevant journal years in association methods, identify what fields most often use association methods, and find out the objectives of researchers using association methods such as FP-Growth and Apriori in various related journals. Using a Systematic Literature Review (SLR) approach, to answer the research questions that have been prepared from the analysis with a total of 33 related journals. So as a result, this study shows that the application of the SLR method can provide answers to the research questions that have been prepared.
Metode dan Algoritma Dalam Sentimen Analisis: Systematic Literature Review Lutfina, Erba; Andriana, Wiwin; Wiratmaja, Sanina Quamila Putri; Febrianti, Ervina
Science Technology and Management Journal Vol. 4 No. 2 (2024): Agustus 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v4i2.274

Abstract

This study highlights the advantages of sentiment analysis using algorithms in understanding public opinion, especially in the context of the increasing complexity of digital content. To investigate and present the latest developments in sentiment analysis, this study uses the Systematic Literature Review (SLR) method to identify and evaluate previously developed sentiment analysis methods. The research steps involve identifying significant sentiment analysis methods, assessing advantages and disadvantages, and critically reviewing recent advances. By applying algorithms in this process, it is expected to be able to analyze and describe the development of sentiment analysis research comprehensively. Through the application of the SLR method, this study is expected to provide in-depth insights into trends, challenges, and opportunities for future research in sentiment analysis, create a better understanding of effective sentiment analysis methods, and detail the expected results that can be expected in the development of sentiment analysis.
Metode dan Algoritma Dalam Data Clustering: Systematic Literature Review Adji, Dian Restu; Lutfina, Erba; Ferdianto, Bhekti Eka; Prashanti, Eva; Anwarri, Kenza Amelia Putri; Prayogo, Syahrul Rizqi
Science Technology and Management Journal Vol. 5 No. 1 (2025): Januari 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i1.326

Abstract

This study is Systematic Literature Review (SLR) of 34 journals related to data grouping techniques (clustering). The main objective of the study is to investigate the use of clustering methods in various research fields. In order to achieve this goal, this study answers five main research questions. First, this study analyzes research fields where clustering methods are often used in data mining applications. Second, this study identifies the most frequently used clustering methods based on data from the journals that have been collected. Third, this study determines the clustering method that provides the most optimal number of groups (clustering) based on the analysis of these journals. Fourth, this study identifies the types of data sets that are most often used in the context of clustering. Finally, this study looks at the distribution of the publication years of these journals to present a time frame of the development of clustering research. The results of this study provide in-depth insight into the trend of the use of clustering methods in various research contexts, provide information on the most commonly used methods, identify methods that provide optimal results, describe the dominant types of data sets, and provide a chronological perspective on the development of clustering research. These findings can provide valuable guidance for researchers interested in applying or developing clustering methods in specific fields.
Implementasi Metode Simple Additive Weighting (SAW) Dalam Mendukung Keputusan Calon Penerima Beasiswa Pada MTS Nurul Ula Febrianti, Ervina; Lutfina, Erba; Saraswati, Galuh Wilujeng; Mahmud, Wildan
Science Technology and Management Journal Vol. 5 No. 2 (2025): Agustus 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i2.350

Abstract

Scholarships are a form of educational aid to support students’ academic pursuits, especially for those with outstanding performance or financial constraints. However, the scholarship recipient selection process at MTS Nurul 'Ula is still conducted manually, which often results in inefficiency and a lack of objective assessment standards. This manual process leads to subjective evaluations, takes a considerable amount of time, and may result in inaccurate scholarship decisions. Therefore, this study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to improve efficiency, accuracy, and transparency in the scholarship selection process. The SAW method was chosen because it allows for clear weighting of each selection criterion, such as academic grades, attitude scores, parental income, number of dependents, and extracurricular achievements. With this system, the selection process can be carried out more objectively based on structured mathematical weighting and calculations. The system is developed using the Delphi and MySQL database, following the Waterfall development model. The results of the study show that the implementation of the SAW-based DSS can accelerate the selection process, reduce subjectivity, and ensure that scholarships are awarded to students who meet the predetermined criteria. Thus, the system is expected to be a fairer, more accurate, and more efficient solution for supporting scholarship selection decisions at MTS Nurul 'Ula. Based on the results of the SAW calculations, an accuracy rate of 85% was achieved, indicating that the system provides accurate and reliable results in determining scholarship recipients.
IMPLEMENTASI METODE ANALYTICAL HIERARCHY PROCESS (AHP) DALAM MENDUKUNG KEPUTUSAN PEMILIHAN REKOMENDASI HANDPHONE Yustiqomah, Evita Citra; Lutfina, Erba; Saraswati, Galuh Wilujeng; Mahmud, Wildan
Science Technology and Management Journal Vol. 5 No. 2 (2025): Agustus 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i2.358

Abstract

In the rapidly evolving digital era, the demand for mobile phones is increasing, with a wide range of specifications and price points available. PT. Topsell Raharja Indonesia, as one of the leading mobile phone retailers, faces challenges in providing fast and accurate recommendations that align with the needs of prospective buyers. The manual phone selection process currently used by sales staff often leads to errors in decision-making, resulting in service delays and customer dissatisfaction. Therefore, this study aims to develop a Decision Support System (DSS) based on the Analytical Hierarchy Process (AHP) to assist the store in recommending optimal mobile phones based on specific criteria. The AHP method is utilized to analyze and compare several key criteria—including price, RAM capacity, camera quality, battery life, performance, and design—in order to determine priority rankings for phone selection. The results demonstrate that the proposed system improves the efficiency of recommendations, reduces selection errors, and accelerates the customer service process. The implementation of the AHP method enables objective and accurate suggestions, achieving up to 98% accuracy. By systematically calculating the weight of each criterion, the system generates optimal alternatives tailored to the preferences and needs of prospective buyers
Co-Authors Adji, Dian Restu Andriana, Wiwin Anwarri, Kenza Amalia Putri Anwarri, Kenza Amelia Putri Caturkusuma, Resha Meiranadi Dimentieva, Imelda Erika Devi Udayanti Febrianti, Ervina Ferdianto, Bhekti Eka Loekito, Joseph Mellati, Pita Prashanti, Eva Prayogo, Syahrul Rizqi Pribadi, Ahmad Naufal Resha Meiranadi Caturkusuma Safa’at, Mochammad Alief Fauzan Akbar Saraswati, Galuh Wilujeng Syahidan, Mukhammad Shaunan Wildan Mahmud Wiratmaja, Sanina Quamila Putri Yuanita, Syafira Putri Yustiqomah, Evita Citra