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All Journal ARABIYAT Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) 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) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Komtika (Komputasi dan Informatika)
Nazir, Alwis
UIN Sultan Syarif Kasim Riau
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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.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes Hamwar, Syahbudin; Nazir, Alwis; Gusti, Siska Kurnia; Iskandar, Iwan; Insani, Fitri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7038

Abstract

Livestock is becoming one of the important animal protein source providers, along with the fisheries sector, to meet the protein needs of the community at large. One type of livestock business that is popular is the maintenance of broiler chickens because of the potential for meat yield. Today, many breeders run a partnership pattern with large companies where breeders play the role of the main supplier and the company as the core. This step helps maintain the stability of production and income of farmers. The success of farmers in broiler chicken production can be measured by looking at the performance index (IP), if the performance is not good then coaching from the core company is needed. The large amount of data obtained from farmers makes it difficult for core companies to model the success rate of farmer production, this can make it difficult for core companies to choose farmers who need coaching. The application of data mining methods using the Naïve Bayes algorithm classification model has the potential to provide solutions to this problem. The purpose of this study was to predict how much success rate of broiler chicken production in Riau region by utilizing the Naïve Bayes Classifier algorithm. This study utilizes a production data set involving 952 broiler chicken farmers in Riau, with 3 scenarios dividing the data ratio of 90:10, 80:20, and 70:30. The results of the analysis showed that through the evaluation of the confusion matrix, it was best found in a data ratio of 90:10 with accuracy results reaching 89,58%, precision reaching 89,89%, and recall reaching 90,16%.
Analisis Perbandingan Algoritma C4.5 dan Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Jamur Rahmadhani, R.; Nazir, Alwis; Syafria, Fadhilah; Afriyanti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7052

Abstract

Mushrooms are organisms that consist of several cells, contain spores, are eukaryotic (have a cell nucleus membrane), and do not have chlorophyll, so fungi depend on other organisms to get food. Mushrooms have very identical shapes, starting with size, shape, smell, and color. So it is difficult for ordinary people to differentiate between poisonous mushrooms and non-poisonous mushrooms. Mistakes in identifying mushrooms can have fatal consequences because they can cause poisoning when consuming mushrooms. Therefore, there is a need for education in classifying poisonous and non-poisonous mushrooms. By applying various classification algorithms, it can be determined which algorithm performs better. In previous research conducted by several researchers on classifying mushrooms, there were differences in the accuracy results for each algorithm. Therefore, this research will raise the question of how to measure or comparion algorithm performance in classification using the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm. The results obtained by comparion the performance of the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm in this research show that the C4.5 algorithm managed to obtain an accuracy level of 98.52%, precision of 98.55%, recall of 98.52%, and f1-score of 98.51%. In contrast, the Modified K-Nearest Neighbor (MKNN) algorithm using the value K=10 achieved an accuracy level of 96.62%, precision of 96.69%, recall of 96.62%, and f1-score value of 96.57%.
Clustering Data Persediaan Barang Menggunakan Metode Elbow dan DBSCAN Berliana, Trisia Intan; Budianita, Elvia; Nazir, Alwis; Insani, Fitri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7089

Abstract

In the world of business and inventory management, efficient inventory management is very important. If a company does not have inventory, it is impossible to fulfill consumer desires. Managing inventory requires careful inventory management and good data analysis. Challenges in inventory involve unpredictable fluctuations in demand, making it difficult to determine optimal inventory levels. Product diversification with various characteristics is also an obstacle, hindering grouping and formulating inventory management strategies. The lack of clear product segmentation adds to the inhibiting factor, making it difficult to identify groups of similar goods. Inefficient stockpiling can be detrimental to the business as a whole, so implementing clustering is necessary to optimize inventory strategies based on product characteristics. By analyzing product groups, companies can develop more efficient and effective inventory management strategies. This research uses a clustering method using the elbow method and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The elbow method is used to determine the most optimal EPS and Minpts values. The aim of this research is to group goods inventory data using the attributes Initial quantity (initial stock), quantity sold (stock sold), and quantity available (available product stock). So that grouped data can make it easier for companies to optimize the inventory of the most sold goods. and fans. Based on the elbow and DBSCAN test results, 144 clusters and 0 noise data were obtained, with cluster 2 being the product with the largest number of sales and inventory. The DBSCAN method which was tested without using elbows obtained cluster 3 results and 959 noise data.
Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11293

Abstract

The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.
Analisis Algoritma Fuzzy C-Means Untuk Pengelompokan Data Keluarga Cahyadi, Wahyu; Haerani, Elin; Nazir, Alwis; Iskandar, Iwan
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8981

Abstract

Mapping the socio-economic conditions of the community plays a crucial role in supporting targeted development planning at the village level. This study aims to apply the Fuzzy C-Means (FCM) algorithm to cluster families in Bina Baru Village based on social, economic, and household environmental indicators. The variables used include family size, income sources, physical condition of the house, basic facilities, as well as monthly expenditure and income levels. This study uses population data from Bina Baru Village, consisting of 1,000 entries with 16 variables. The FCM algorithm was chosen for its ability to accommodate multiple degrees of membership (fuzzy membership), making it more adaptable in capturing the diversity and ambiguity of socio-economic characteristics. The results show that FCM produces two main clusters: Cluster 0, with 440 members, reflects families with middle to lower economic conditions, permanent housing, and adequate basic facilities; and Cluster 1, with 560 members, represents families with lower economic conditions, semi-permanent housing, and relatively smaller family sizes. Evaluation using the Xie–Beni index (35.4976), Fuzzy Partition Entropy (0.6843), and Fuzzy Cluster Index (0.4468) indicates that the two-cluster model has the best clustering quality compared to other numbers of clusters. Overall, the Fuzzy C-Means algorithm is effective in mapping variations in family welfare and can be used as a basis for formulating development policies and data-driven community empowerment programs in Bina Baru Village.
Data Mining for Analyzing Consumer Segmentation: Identifying Consumer Preference Patterns Using the Fuzzy C-Means Clustering on Halal Products Iskandar (Scopus ID: 55316114000), Iwan; Nazir, Alwis
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38608

Abstract

Halal products are increasingly popular worldwide, not only in Muslim-majority countries but also in non-Muslim nations.  The global halal market exceeds USD 650 million annually, emphasizing the importance of halal certification, particularly in Indonesia as the world’s largest Muslim-majority country. This research aims to cluster consumers of halal meat products by analyzing factors influencing consumer behavior in purchasing certified halal beef and chicken. The study employs the Fuzzy C-Means (FCM) clustering algorithm on 176 respondents’ questionnaire data consisting of 36 parameters. The experiment was performed using Google Colab with a maximum of 1000 iterations, error tolerance of 0.0001, and fuzziness coefficient m = 2.4. Results show that two optimal clusters were formed, with a Partition Coefficient Index (PCI) value of 0.9993, indicating excellent clustering quality. The first cluster consists primarily of young consumers aged 15–24 with lower spending, while the second cluster includes adults aged 35–54 with higher income. Both groups prioritize halal certification and logo visibility when choosing meat products. The findings provide insights for halal product retailers and policymakers to enhance halal product distribution, certification support, and marketing strategies in Indonesia.
Analisis Clustering Menggunakan Metode K-Means untuk Mengidentifikasi Pola Kepuasan Alumni: Clustering Analysis Using the K-Means Method to Identify Alumni Satisfaction Pattern Ramadhan, Muhammad Ilham; Nazir, Alwis; Irsyad, Muhammad; Sanjaya, Suwanto; Syafria, Fadhilah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2401

Abstract

Tracer study berperan penting dalam mengevaluasi kualitas layanan pendidikan berdasarkan pengalaman alumni. Analisis kepuasan alumni terhadap fasilitas pembelajaran umumnya masih terbatas pada statistik deskriptif, sehingga belum mampu mengungkap pola kepuasan secara tersegmentasi pada data berskala besar. Penelitian ini bertujuan untuk mengidentifikasi pola segmentasi kepuasan alumni terhadap fasilitas pembelajaran di Universitas Islam Negeri Sultan Syarif Kasim Riau (UIN Suska Riau) sebagai indikator penting dalam evaluasi kualitas layanan pendidikan. Metode yang digunakan adalah K-Means Clustering, diimplementasikan melalui tahapan Knowledge Discovery in Database (KDD) pada 6.508 data tracer study alumni S1 lulusan 2010–2023. Proses preprocessing mencakup normalisasi data numerik menggunakan Min-Max Scaling untuk menyamakan skala enam indikator kepuasan (Perpustakaan, Teknologi Informasi, Modul Belajar, Ruang Belajar, Laboratorium, dan Variasi Mata Kuliah), sehingga meminimalkan bias dalam perhitungan jarak Euclidean. Berdasarkan Elbow Method, diperoleh jumlah klaster optimal adalah K=3, dan kualitas pengelompokan divalidasi dengan nilai Davies-Bouldin Index (DBI) sebesar 0,874, mengonfirmasi stabilitas klaster yang terbentuk. Analisis menghasilkan tiga klaster berbeda: Klaster 0 (Tingkat Kepuasan Tinggi) yang dominan, Klaster 1 (Tingkat Kepuasan Rendah), dan Klaster 2 (Tingkat Kepuasan Sangat Tinggi). Hasil ini memberikan segmentasi kepuasan yang eksplisit sebagai dasar bagi universitas untuk merumuskan strategi peningkatan fasilitas secara terarah dan berkelanjutan.
KLASIFIKASI MINAT BACA MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Hafiz, Muhammad; Budianita, Elvia; Nazir, Alwis; Gusti, Siska Kurnia
Journal of Economic, Bussines and Accounting (COSTING) Vol. 9 No. 1 (2026): COSTING : Journal of Economic, Bussines and Accounting
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/gcqhr236

Abstract

Minat baca merupakan faktor penting dalam mendukung keberhasilan akademik siswa, namun pengukurannya masih sering dilakukan secara subjektif. Penelitian ini bertujuan untuk mengklasifikasikan tingkat minat baca siswa MTsN 1 Payakumbuh menggunakan metode Naïve Bayes Classifier. Atribut yang digunakan sebagai input berupa 20 butir pernyataan kuesioner skala Likert yang merepresentasikan kebiasaan, frekuensi, motivasi, serta preferensi membaca siswa. Data penelitian diperoleh dari 911 responden yang dikelompokkan ke dalam tiga kelas tingkat minat baca, yaitu tinggi (329 data), sedang (501 data), dan rendah (81 data). Pengujian model dilakukan menggunakan tiga skema pembagian data latih dan data uji, yaitu 90:10, 80:20, dan 70:30. Evaluasi performa model menggunakan confusion matrix menunjukkan bahwa skema 90:10 menghasilkan akurasi sebesar 96,74%, skema 80:20 sebesar 97,81%, dan skema 70:30 sebesar 98,18%. Hasil tersebut menunjukkan bahwa metode Naïve Bayes Classifier memiliki performa yang sangat baik dan konsisten dalam mengklasifikasikan tingkat minat baca siswa berdasarkan data kuesioner.
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 Cahyadi, Wahyu 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 Iskandar (Scopus ID: 55316114000), Iwan Isra Almahsa, Muhammad Iwan Iskandar Jasril Jasril Mahyudin Ritonga Muhammad Abdillah Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Mulyati, Sabar Mulyono, Makmur Najmi, Risna Lailatun Nurfadilah, Nova Siska Oktavia, Lola Pizaini Pizaini Putri. P, Zurneli Kurnia Rahmadhani, R. Ramadhan, Muhammad Ilham Reni Susanti Sri Wahyuni Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syaputra, Muhammad Dwiky Wiranti, Lusi Diah Yusra Yusra, - Yusra, Yusra