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All Journal ARABIYAT Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Knowbase : International Journal of Knowledge in Database Jurnal Komtika (Komputasi dan Informatika)
Nazir, Alwis
UIN Sultan Syarif Kasim Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics

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Journal : Building of Informatics, Technology and Science

 1 
Analisis Pola Asosiasi Data Transaksi Penjualan Minuman Menggunakan Algoritma FP-Growth dan Eclat Najmi, Risna Lailatun; Irsyad, Muhammad; Insani, Fitri; Nazir, Alwis; ., Pizaini
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3592

Abstract

Every day transaction activities between companies and consumers continue to be carried out. This makes transaction data more and more and accumulate. This transaction data can be processed into more useful information using technology. Data mining is a technology that can work on a collection of transaction data into information that can be taken by companies as decision makers. The association rule method is used as a method to see the relationship between items in a transaction data. To analyze transaction data, researchers used the FP-Growth and Eclat algorithms. There are three stages of association in this study which are distinguished from the confidence value. The results in the first stage have a minimum confidence value of 0.4, the FP-Growth algorithm produces 41 association pattern rules, while the Eclat algorithm produces 32 association pattern rules. Then in the second stage the minimum trust value is 0.5, the FP-Growth algorithm produces 40 association pattern rules, for the Eclat algorithm it produces 32 association pattern rules. In the third stage, the minimum trust value is 0.6, the FP-Growth algorithm generates 32 association pattern rules, while the Eclat algorithm generates 30 association pattern rules. The results of the association pattern rules show that the Eclat algorithm is more efficient in determining the association pattern rules than the Fp-Growth algorithm
Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor Novitasari, Fitria; Haerani, Elin; Nazir, Alwis; Jasril, Jasril; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3610

Abstract

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.
Klasifikasi Sentimen Terhadap Pengangkatan Kaesang Sebagai Ketua Umum Partai PSI Menggunakan Metode Support Vector Machine .Safrizal, Safrizal; Agustian, Surya; Nazir, Alwis; Yusra, Yusra
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5340

Abstract

The appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) has sparked various responses on social media, particularly on Twitter. This research aims to classify public sentiment regarding the appointment using the Support Vector Machine (SVM) algorithm with FastText feature representation. The data used for classification involves a small training dataset. The text preprocessing process includes cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. FastText word embedding is used to convert words into vectors, and an SVM model with Grid Search is used for parameter tuning to obtain the optimal model. The use of external datasets to expand the initially limited training dataset enhances data representation and improves the model's performance in sentiment classification. The Covid dataset was expanded by adding 100, 200, and 300 tweets for each negative, positive, and neutral label. From the experiments conducted, the best accuracy on the test data was found in experiment ID C2 with an F1-Score of 53.59% and an accuracy of 62.73%. In experiment ID C3 with the same dataset, the F1-Score was 50.46% and the accuracy was 60.46%. Finally, in experiment ID C7 with the same dataset, the F1-Score was 47.19% and the accuracy was 53.09%.
Pengelompokan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma K-Medoids Nurfadilah, Nova Siska; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7409

Abstract

Academic stress is one of the common problems issues by university students due to heavy with heavy workloads, grade pressure, and various academic This condition can have a negatively impact on mental health, productivity and overall academic performance. In the long term, unmaged stress may lead serious psychological disorders. Therefore, it is important to accurately identify and classify the levels of academic stress. This study aims to cluster students’ academic stress levels by utilizing the K-Medoids algorithm. The data analyzed in the research were collected through questionnaires that were filled out by 507 students from the 2021-2023 cohorts, based on a modified version of the Perception of Academic Stress Scale (PASS). The results show that the K-medoids algorithm successfully clustered the data in 2 groups: cluster 0, which represents a moderate stress level with 212 students, and cluster 1, which indicates a high stress level with 295 students. This high-stress cluster exhibited higher average cores on questions 12 and 13 (score 3-5), which fall under the favorable category and are suspected to be the main triggers of academic stress among students in this group. Based on two evalutation metrics-Silhouette Coeficient and Davies-Bouldin Index (DBI)-it can be concluded that the optimal number of clusters for this data set is K=2. However, the clustering separation was not optimal due to he variation in study programs and the uneven distribution of respondets across academic years. This research is expected to provide direction the development intervation policies and strategies to support student welfare.
Penerapan Algoritma K-Means Untuk Mengelompokkan Tingkat Stres Akademik Pada Mahasiswa Wiranti, Lusi Diah; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7410

Abstract

Academic stress is a prevalent concern among university students, often arising from various challenges within the academic environment. These challenges may include tight assignment deadlines, elevated expectations from both lecturers and parents, ineffective time management, and negative self-assessment. If left unaddressed, such stress can negatively impact students’ academic performance and mental well-being. This study focuses on categorizing student academic stress levels using the K-Means clustering algorithm. Data were collected from 507 participants through a customized version of the Perception of Academic Stress Scale (PASS) questionnaire, adapted to suit the study context. Prior to analysis, the data were preprocessed and converted into a numerical format. Clustering was performed using Python on the Google Colab platform. To assess the clustering performance, two evaluation metrics were used: the Davies-Bouldin Index (DBI) and the Silhouette Coefficient. Lower DBI values suggest that the clusters formed are more compact and distinct from each other, while higher Silhouette values indicate better clustering performance. From the evaluation, the best clustering result was found when the number of clusters was 2, with a DBI score of 1.43 and a Silhouette score of 0.27. Nonetheless, these values still fall short of the ideal range, likely due to the heterogeneous nature of the data, as participants came from five different departments within the Faculty of Science and Technology. Moreover, the number of responses varied across academic years (2021–2023). Cluster 1 comprised 229 students identified as having low levels of academic stress, as shown by their lower questionnaire scores. In contrast, Cluster 2 consisted of 278 students with higher levels of stress, as reflected in their higher scores (ranging from 3 to 5) on positively worded items.
Co-Authors .Safrizal, Safrizal Abdul Wahid Adzhima, Fauzan Afriyanti, Iis Afriyanti, Liza Agnesti, Syafira Akbar, Lionita Asa Akhyar, Amany Ananda, Rahmadan Alam Ardan Baehaqi Berliana, Trisia Intan Dinata, Ferdian Arya Eka Pandu Cynthia Elin Haerani Elvia Budianita Fadhilah Syafria Fauzan Adzim Fitri Insani Fitria Novitasari Gusti, Siska Kurnia Habibi, M. Ilham Hamwar, Syahbudin Hasibuan, Aldiansyah Pramudia Iis Afrianty Isra Almahsa, Muhammad Jasril Jasril Mahyudin Ritonga Muhammad Abdillah Muhammad Fikry Muhammad Irsyad Mulyati, Sabar Mulyono, Makmur Najmi, Risna Lailatun Nurfadilah, Nova Siska Oktavia, Lola Pizaini Pizaini Putri. P, Zurneli Kurnia Rahmadhani, R. Reni Susanti Sri Wahyuni Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syaputra, Muhammad Dwiky Wiranti, Lusi Diah Yusra Yusra, - Yusra, Yusra