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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) SEIKO : Journal of Management & Business Jiko (Jurnal Informatika dan komputer) Journal of Secretary and Business Administration MIND (Multimedia Artificial Intelligent Networking Database) Journal Progresif: Jurnal Ilmiah Komputer Syntax Idea Jurnal Ilmiah Edunomika (JIE) E-Link: Jurnal Teknik Elektro dan Informatika JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Darma Agung JOINT (Journal of Information Technology Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat Indonesia JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence Prosiding Konferensi Nasional PKM-CSR Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Engineering Continuity International Journal of Multidisciplinary Approach Research and Science Jurnal Sekretaris & Administrasi Bisnis
Sulianta, Feri
Teknik Informatika, Universitas Widyatama
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Penerapan Association Rule Mining untuk Rekomendasi Promo Bundling dalam Sistem CRM Berbasis Online Herdian, Gentala Virgiawan; Sulianta, Fery
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2398

Abstract

The online ordering system is an important strategy in improving service efficiency and customer loyalty, especially in micro businesses such as Coffee Kane. This study applies the Association Rule Mining (Apriori) algorithm within the CRISP-DM framework to identify customer purchasing patterns and design bundling promotions based on Customer Relationship Management (CRM). The data used is transaction history from the last two months. The analysis results produced a number of significant association rules, such as product combinations with the highest lift value of 31.50. These rules were implemented into the Laravel-based ordering system and automatically displayed to customers. This study shows that this data-driven approach not only improves the effectiveness of promotions but also strengthens customer engagement through an adaptive and personally relevant system.
Sistem Rekrutmen Online Berbasis TOPSIS untuk Seleksi Kandidat Faturrohman, Adit; Sulianta, Feri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2430

Abstract

An efficient and accurate recruitment process is crucial for companies to ensure the quality of human resources that support growth and innovation. For Coffee Kane, acquiring the best talent is key to maintaining competitive advantage. However, traditional recruitment methods often face challenges, such as time inefficiency, high operational costs, and limitations in the search for quality candidates. This study proposes a web-based online recruitment system that integrates the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This system is designed to accelerate the selection process, reduce operational costs, and expand the search for candidates without geographical limitations. The implementation of this system has successfully automated the selection process, increased efficiency, and resulted in more accurate, objective, and transparent decision-making in selecting the best candidates.
Basis Data Semu Menggunakan Lembar Kerja Elektronik pada Sistem Otomatisasi Perkantoran Feri Sulianta
Jurnal Sekretaris dan Administrasi Bisnis Vol 1 No 1 (2017): Jurnal Sekretaris dan Administrasi Bisnis
Publisher : LPPM Universitas Taruna Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31104/jsab.v1i1.9

Abstract

The development of information technology nowadays encourages users to use the software appropriately so that commercial software can be maximally empowered that contribute to the efficiency and effectiveness of the job. To support its work, users use Microsoft's Microsof Excel Office Automation System worksheet commonly used by end users for clerical, repetitive and independent work. Microsoft Excel applications are generally used for office worker, clerical computation and users generally use only a portion of the features of some features owned by the application. In this case, the user can maximize the ability of Excel by creating an interactive pseudo database using only Microsoft Excel, so users can organize data with automated database schema. It is intended for time efficiency and also improves the data security factor of human error in handling data. Keywords: Office Automation System, Pseudo Database, Electronic Worksheet, Automatic
OPTIMIZING TRANSFORMER-BASED LEARNING MODEL WITH TABTRANSFORMER FOR PREDICTING ANTIBIOTIC SUSCEPTIBILITY FROM MICROBIOLOGY MEDICAL RECORDS Sulianta, Feri; Amalia, Endang; Samiharjo, Rosalin; Herdinata, Noval Eka
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7582

Abstract

Antimicrobial Resistance (AMR) has become a growing threat due to the increase in infections that are unresponsive to conventional therapies. Therefore, the development and optimization of Transformer-based Deep Learning using TabTransformer was employed to model the complex interactions between categorical features. This model was trained to predict antibiotic susceptibility at the individual culture level using the Antibiotic Resistance Microbiology Dataset (ARMD). To address the challenge of highly imbalanced data, the methodology applied includes extensive feature engineering to create historical and clinical variables, as well as the use of Focal Loss during training. After optimization, the final model demonstrated excellent discriminatory ability, with an Area Under the ROC Curve (AUC-ROC) of 0.93 and balanced classification performance, yielding a macro average F1-score of 0.82. Interpretability analysis using SHAP confirmed that patient clinical history and prior drug exposure were the most dominant predictive factors. These findings suggest that the Transformer-based Deep Learning architecture using TabTransformer, combined with clinically relevant feature engineering, can produce a reliable and evidence-based predictive tool
Digital Marketing Maturity Models: A Comprehensive Literature Review Amalia, Endang; Sulianta, Feri; Nugraha, Ucu
International Journal of Multidisciplinary Approach Research and Science Том 4 № 01 (2026): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v4i01.2087

Abstract

Digital marketing has become a critical enabler of organizational transformation, requiring firms to adopt customer-centric strategies and optimize processes in increasingly complex environments. However, the conceptualization and measurement of digital marketing maturity remain inconsistent across the literature. This study addresses this gap by conducting a Systematic Literature Review (SLR) of evolutionary digital marketing maturity models published between 2020 and 2024. Following a four-stage methodological framework—comprising selection and exclusion criteria, database search, quality assessment, and data extraction and synthesis—the review identifies similarities, differences, strengths, and limitations among existing models. The findings reveal that while current models provide useful structures for guiding digital transformation, they lack a precise conceptual definition and standardized measurement framework. The study contributes theoretically by advancing the operationalization of digital maturity and practically by offering executives a framework to assess, benchmark, and monitor progress in digital transformation. Furthermore, it establishes a foundation for future research on the relationship between digital maturity and corporate performance across cultural and regional contexts.
ATURAN ASOSIASI MENGGUNAKAN ALGORITMA APRIORI UNTUK MENCIPTAKAN STRATEGI PEMASARAN PADA APOTEK Feri Sulianta; Eriko Prayogo
E-Link: Jurnal Teknik Elektro dan Informatika Vol. 19 No. 1: Mei 2024
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/e-link.v19i1.5951

Abstract

The sales of pharmaceutical products among the public are increasing, especially with the recent pandemic that has led to an increase in drug sales. This has created significant potential for the pharmaceutical industry or drug sales businesses. However, proper marketing plans are required in the pharmaceutical industry to optimize revenue. Analyzing drug sales trends can provide valuable insights for creating excellent marketing plans. To develop a superior marketing plan, an analysis of sales transaction data is necessary with the help of data mining, which is useful for obtaining important information from the dataset being analyzed. The Apriori algorithm is used in this research to examine association rule patterns of drug sales in pharmacies The sales information used as dataset is consisting of 600,000 transactional data collected over six years (2014–2019). This dataset includes the date and time of sales, pharmaceutical drug brands, and other relevant information.
Co-Authors Ahmad Arif Ahmad Zaeni, Ahmad Ai Rosita Amalia, Endang Ari Purno Wahyu Wibowo Aswari Aswari Daud Rifki Gunawan Deden Farhan Dzikriyana Zuhdia Aziz Eriko Prayogo Esa Fauzi Fadllur Rohman, Muhammad Naufal Fathur Rizki, Muhamad Faturrohman, Adit Fitrah Rumaisa Galang Raka Siwi Herdian, Gentala Virgiawan Herdinata, Noval Eka Kautsar Baihaqi Supriyadi Mendonca, Fernando Muharam, Rahman Mutaqin, Safira Mardiyah Philothra Clarissa Raina Pranata, Aryoga Putri, Vianty Julia Suryana Rachmat, Alam Maulana Rah Utami Nugrahani Rahman Muharam Raina, Philothra Clarissa Reza Syafrian Setiadi Rizky Muhammad Fauzi Rosita, Ai Samiharjo, Rosalin Saputra, Rinaldy Buana Sriyani Violina Suryaresmana, Rizka Putera SY Yuliani Ucu Nugraha Ulfah, Khaerani yakub, sy yuliani yuliani Yan Puspitarani Yenie Syukriyah