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All Journal Infotech Journal Jurnal Ilmiah Universitas Batanghari Jambi Faktor Exacta Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Riset Informatika INTI Nusa Mandiri Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Pendidikan Teknologi Informasi dan Vokasional Jurnal Infortech JIRA: Jurnal Inovasi dan Riset Akademik Jurnal AbdiMas Nusa Mandiri HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Dedikasi: Jurnal Pengabdian kepada Masyarakat TIERS Information Technology Journal Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Teknologi Sistem Informasi Abdi Teknoyasa Journal of Artificial Intelligence and Digital Business COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Komputer Antartika Jurnal Pengabdian Masyarakat Jurnal Cendekia Ilmiah

Fachri Amsury

Universitas Nusa Mandiri

Author-ID : 3447608

Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementasi Machine Learning Tanpa Label (Unsupervised) dalam Identifikasi dan Klasifikasi Penyakit Berdasarkan Data Medis Pasien Jody, Pradithia; Sucahyo, Muhamad Yusuf; Setiawan, Rizqi; Prasetyo, Dwi Bagus; Amsury, Fachri; Fahlapi, Riza
Jurnal Ilmiah Universitas Batanghari Jambi Vol 26, No 1 (2026): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v26i1.6402

This study aims to implement an unsupervised learning method using the K-Means Clustering algorithm to group patients based on medical data without requiring prior disease labels. The dataset used consists of 300 simulated patient data (synthetic data) with variables of blood pressure, blood sugar, cholesterol, and symptoms of fever, cough, shortness of breath, and muscle pain. The results show that the model can divide patients into four main clusters: hypertension, diabetes, hypercholesterolemia, and respiratory infections, which are consistent with realistic clinical conditions. Analysis of the average feature per cluster, scatter plots, and heatmaps strengthen the interpretation of the characteristics of each group. This approach proves that the K-Means method can be an efficient early diagnostic tool even though the data is unlabeled.

CLASSIFICATION OF STUDENT SATISFACTION WITH ONLINE LECTURE Ruhyana, Nanang; Mardiana, Tati; Amsury, Fachri; Sulistyowati, Daning Nur
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high.

COMPARISON OF CLASSIFICATION ALGORITHMS FOR ANALYSIS SENTIMENT OF FORMULA E IMPLEMENTATION IN INDONESIA Amsury, Fachri; Ruhyana, Nanang; Mardiana, Tati
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

The Formula E racing series has become one of the world's most prestigious competitions. In 2022, Indonesia hosted the famous Formula E race. The event possesses the potential for economic benefits for Indonesia worth 78 million euros through the arrival of 35,000 spectators. Indonesians are enthusiastic about Formula E since it allows their nation to encourage tourists and gain international prominence. However, some people do not support this event. Since they regard that amid the COVID-19 pandemic, it is preferable for the government to focus on people affected by the pandemic rather than support a Formula E event. This study compares the Support Vector Machine and Naive Bayes algorithms in classifying public opinion in the Formula E race. This study gets its information from user comments on social media platforms, especially Twitter. The stages start with text preprocessing and include cleaning, case folding, tokenization, filtering, and stemming. Proceed with weighting using the TF-IDF approach. Data testing uses a confusion matrix to evaluate the classification results by testing accuracy, precision, and recall. Categorizing public opinion using the SVM algorithm has an accuracy of 82 percent, a precision of 97.86 percent, and a recall of 77.90 percent. On the other hand, the accuracy of the Naive Bayes technique is more limited, at 87.54 percent. Society's opinion on Twitter shows positive sentiment towards implementing Formula E.

Implementation of the Association Method in the Analysis of Sales Data From Manufacturing Companies Amsury, Fachri; Ruhyana, Nanang; Riyadi, Andry Agung; Rahman, Ihsan Aulia
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

The company produces sales data every day. Over time, the data increases, and the amount becomes very large, and the data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. Sale. In order to overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the Knowledge Discovery in Database concept using the association method and apriori algorithm and a supporting application, namely RapidMiner. This study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. The results of this study produce 316 association rules where the best rules are generated on record 309 with PRO 889 & PRO 868 PRO 869 rules.

PENERAPAN CLUSTERING K-MEANS UNTUK SEGMENTASI PELANGGAN PADA BISNIS RETAIL: APPLICATION OF K-MEANS CLUSTERING FOR CUSTOMER SEGMENTATION IN RETAIL BUSINESSES Fahsya, Lucky Chairul; Wijaya, Chandra; Bintang, Firsta Maha; Mulyono, Justine James; Ramadhan, Fitrah; Amsury, Fachri
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p38-52

Perkembangan bisnis retail online yang semakin pesat menuntut perusahaan untuk memahami perilaku pelanggan secara lebih mendalam agar dapat merancang strategi pemasaran yang efektif. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan berdasarkan pola transaksi dengan menggunakan metode K-Means Clustering. Data yang digunakan merupakan data sekunder dari Online Retail Dataset yang diperoleh melalui UCI Machine Learning Repository, yang berisi catatan transaksi 4.338 pelanggan dari sebuah toko online di Inggris. Tahapan penelitian meliputi data preprocessing, pembentukan variabel Recency, Frequency, Monetary (RFM), standarisasi data, dan penerapan algoritma K-Means dengan jumlah cluster (k) = 3. Hasil penelitian menunjukkan bahwa pelanggan terbagi ke dalam tiga kelompok utama: pelanggan loyal (0,3%), potensial (74,8%), dan pasif (24,9%). Validitas clustering dikonfirmasi melalui tiga metrik evaluasi dengan Silhouette Score 0,602, Davies-Bouldin Index 0,756, dan Calinski-Harabasz Score 3.124,58. Cluster loyal berkontribusi 18,4% dari total revenue meskipun hanya 0,3% populasi. Penerapan metode K-Means terbukti efektif dalam mengidentifikasi pola perilaku pelanggan yang dapat dimanfaatkan untuk menentukan strategi retensi dan promosi yang lebih tepat sasaran. The rapid growth of online retail businesses requires companies to deeply understand customer behavior in order to design effective marketing strategies. This study aims to perform customer segmentation based on transactional patterns using the K-Means Clustering method. The dataset used is secondary data obtained from the Online Retail Dataset available in the UCI Machine Learning Repository, containing transaction records of 4,338 customers from a UK-based online store. The research stages include data preprocessing, construction of Recency, Frequency, Monetary (RFM) variables, data standardization, and implementation of the K-Means algorithm with the number of clusters (k) set to three. The results show that customers are grouped into three main segments: loyal customers (0.3%), potential customers (74.8%), and passive customers (24.9%). Clustering validity is confirmed through three evaluation metrics with Silhouette Score of 0.602, Davies-Bouldin Index of 0.756, and Calinski-Harabasz Score of 3,124.58. The loyal cluster contributes 18.4% of total revenue despite representing only 0.3% of the population. The application of the K-Means method proves effective in identifying customer behavior patterns that support management in developing more targeted retention and promotional strategies.

ANALISIS KESADARAN MAHASISWA TERHADAP PRIVASI DATA DENGAN MENGGUNAKAN METODE NAÏVE BAYES: ANALYSIS OF STUDENTS’ AWARENESS OF DATA PRIVACY USING THE NAÏVE BAYES METHOD Septia, Kaman; Fhadila, Loade Thoriq; Syahril, Muhammad Irvan; Sukarno, Chesario; Nazara, Iman Kasih; Amsury, Fachri
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p29-37

Privasi data merupakan aspek penting dalam aktivitas digital, terutama bagi mahasiswa yang aktif menggunakan berbagai platform daring. Penelitian ini bertujuan menganalisis tingkat kesadaran privasi data mahasiswa menggunakan algoritma Naïve Bayes. Data primer dikumpulkan melalui kuesioner Google Form yang berisi 13 indikator kesadaran privasi dan disebarkan melalui media sosial dengan teknik voluntary response sampling. Sebanyak 56 mahasiswa berpartisipasi sebagai sampel penelitian. Pengolahan data mengikuti tahapan Knowledge Discovery in Database (KDD), meliputi seleksi data, pembersihan, transformasi, pemodelan, serta evaluasi. Transformasi dilakukan dengan menghitung skor total per responden dan mengelompokkan tingkat kesadaran ke dalam kategori “Tinggi” dan “Standar” menggunakan cut-off empiris untuk menjaga keseimbangan kelas. Analisis klasifikasi dilakukan menggunakan algoritma Naïve Bayes melalui aplikasi Orange Data Mining, dengan evaluasi menggunakan Test and Score serta Confusion Matrix. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan tingkat kesadaran privasi dengan akurasi 91.1%, precision 92.6%, recall 91.1%, F1-score 91.5%, AUC 0.976, dan MCC 0.738. Temuan ini menunjukkan bahwa Naïve Bayes efektif dalam mengenali pola kesadaran privasi mahasiswa dan layak digunakan sebagai dasar pengembangan program edukasi privasi data di lingkungan perguruan tinggi. Data privacy is a critical aspect of digital activity, particularly for university students who frequently engage with online platforms. This study aims to analyze students’ awareness of data privacy using the Naïve Bayes classification algorithm. Primary data were collected through a Google Form questionnaire consisting of 13 indicators of privacy awareness and distributed via social media using a voluntary response sampling technique. A total of 56 students participated in this study. Data processing followed the Knowledge Discovery in Database (KDD) stages, including data selection, cleaning, transformation, modeling, and evaluation. The transformation process involved calculating the total awareness score for each respondent and categorizing awareness levels into “High” and “Standard” using an empirical cut-off to maintain class balance. The Naïve Bayes algorithm was applied using the Orange Data Mining application, with performance evaluated through the Test and Score and Confusion Matrix tools. The results indicate that the model performed effectively, achieving an accuracy of 91.1%, precision of 92.6%, recall of 91.1%, F1-score of 91.5%, AUC of 0.976, and MCC of 0.738. These findings demonstrate that Naïve Bayes is suitable for analyzing student privacy awareness patterns and can serve as a foundation for designing educational interventions to improve privacy literacy in academic environments.

PENERAPAN ALGORITMA APRIORI UNTUK ANALISIS POLA PEMILIHAN MENU DI RH STORE: IMPLEMENTATION OF THE APRIORI ALGORITHM FOR ANALYZING MENU SELECTION PATTERNS AT RH STORE Pratama, Dimas Limanov; Kristy, Natasya; Saputra, Rayhan Daffananda; Ihsan, Muhammad Awaluddin Azhari; Amsury, Fachri; Supendar, Hendra
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p19-28

Fluktuasi penjualan yang dialami RH Store menunjukkan perlunya pemanfaatan data transaksi secara optimal untuk mendukung pengambilan keputusan bisnis. Selama ini, data transaksi penjualan belum dimanfaatkan secara maksimal untuk mengidentifikasi pola pemilihan menu pada data transaksi penjualan RH Store. Metode yang digunakan adalah pendekatan kuantitatif deskriptif dengan mengikuti tahapan Knowledge Discovery in Databases (KDD), meliputi seleksi data, pembersihan data, transformasi ke bentuk market basket, serta pembentukan aturan asosiasi. Data yang digunakan berupa 101 transaksi penjualan pada periode Juli hingga September 2025 dan dianalisis menggunakan aplikasi Orange Data Mining. Pengujian dilakukan dengan beberapa kombinasi nilai support dan confidence, yaitu 30%-60%, 40%-80%, dan 50%-90%. Hasil penelitian menunjukkan bahwa pada nilai support 50% dan confidence 90% diperoleh 17 aturan asosiasi dengan nilai confidence tertinggi sebesar 95% dan seluruh nilai lift lebih besar dari 1. Produk Roti Tumpuk, Roti Bulat, dan Roti Kukus memiliki tingkat keterkaitan paling dan sering muncul sebagai consequent. Hasil analisis ini dapat dimanfaatkan sebagai dasar dalam penyusunan menu paket, strategi promosi, serta pengelolaan persediaan produk di RH Store. Sales fluctuations experienced by RH Store indicate the need to optimize the use of transaction data to support business decision-making. To date, sales transaction data have not been fully utilized to identify menu selection patterns at RH Store. This study employs a descriptive quantitative approach following the Knowledge Discovery in Databases (KDD) stages, including data selection, data cleaning, transformation into a market basket format, and association rule generation. The dataset consists of 101 sales transactions collected from July to September 2025 and was analyzed using Orange Data Mining. Experiments were conducted using several combinations of support and confidence thresholds, namely 30%–60%, 40%–80%, and 50%–90%. The results show that at a support threshold of 50% and a confidence threshold of 90%, 17 association rules were generated, with the highest confidence value reaching 95% and all lift values exceeding 1. The products Roti Tumpuk, Roti Bulat, and Roti Kukus exhibit the strongest associations and frequently appear as consequents. These findings can be utilized as a basis for designing menu packages, promotional strategies, and inventory management at RH Store.

Co-Authors Adiputra, Jason Adiputra, Mahesa Aditya, Tommy Ahmad Fadlil Fauzi Alghifari, Luthfi Adam Andri Agung Riyadi Anggi Dian Oktavianingsih ANGGIE ARDIANSYAH Anjani, Mutiara Putri Asrul Azalia, Devina Bayhaqy, Achmad BENNI RAMADHAN Bintang, Firsta Maha Chandra Wijaya Dwiza Riana Fahlapi, Riza Fahsya, Lucky Chairul Fatihah, Cinta Aprilia Febriyanti, Syafvika Tiara Ferdy Saputra Fhadila, Loade Thoriq Frieyadie Gunawan, Heru HANAFI EKO DARONO Hanifah, Nida Helmalia Putri Ismayani Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Hernawati Ibrahim, Akbar Ida Ayu Putu Sri Widnyani Ihsan, Muhammad Awaluddin Azhari Ika Kurniawati Ika Kurniawati Ika Kurniawati Intan Permatasari Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Jody, Pradithia Juan Immanuel Jupriyanto . Kristy, Natasya Muhammad Ilyas Muhammad Rizki Fahdia Muhammad Rizki Fahdia MUHAMMAD RIZKI FAHDIA Muhammad Rizki Fahdia Mulyono, Justine James nanang ruhyana Nanang Ruhyana Nanang Ruhyana Nanang Ruhyana Nanjaya, Ahmad Fadhil Nazara, Iman Kasih Nurajijah Nurajijah Oktavia, Devya Septi Ongki D.Simatupang Pangestu, Ridwan Panggabean, Gempar Galang Al Fallah Prasetyo, Dwi Bagus Pratama, Dimas Limanov Putria Pebriana Sitanggang Rachimsah, Wildan RAHMAD SINGGIH AJI PAMBUDI Rahman, Ihsan Aulia Ramadhan, Fitrah Rasam Rasam Riyadi, Andri Agung Riyadi, Andry Agung Riza Fahlapi Rizki Fahdia, Muhammad ruhyana, nanang Rusdiansyah, Irfandi Saputra, Aden Asywak Saputra, Irwansyah Saputra, Rayhan Daffananda Satria, Fauzan Septia, Kaman Setiawan, Rizqi Siti Fauziah Siti Fauziah Sucahyo, Muhamad Yusuf Sukarno, Chesario Sulistyowati, Daning Nur Syahril, Muhammad Irvan Syahrur Rhamadan Tati Mardiana Tati Mardiana, Tati Tue Rebong, Hendrikus Vivi Rahayu Yusnia Budiarti Zhafran, Muhammad Faiz

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