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All Journal Semantik Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JURNAL IT: Media Infromasi STMIK Handayani Makassar Journal of Technopreneurship and Information System (JTIS) Building of Informatics, Technology and Science TEPIAN JPM: JURNAL PENGABDIAN MASYARAKAT Buletin Poltanesa Bulletin of Computer Science Research Journal of Social Responsibility Projects by Higher Education Forum J-SAKTI (Jurnal Sains Komputer dan Informatika) Bulletin of Information Technology (BIT) J-Intech (Journal of Information and Technology) Informatika Wicida Jurnal Teknik Informatika Journal of Artificial Intelligence and Digital Business
Ita Arfyanti
Jurusan Sistem Informasi STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Transportation Other

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Analysis of Content Engagement Rate on Wicida Tv's Instagram Account Vania Diva Az-zira; Ita Arfyanti; Hanifah Ekawati
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3825

Abstract

Wicida Tvis one of the UKM (Student Activity Units) at STMIK Widya Cipta Dharma. The UKM has various social media and in this study the Wicida TvInstagram account was chosen, which aims to analyze the level of interaction with followers and other users on the content published on the Wicida Tv’s Instagram account. By using a mix method approach, with a focus on quantitative calculations for engagement rate results and qualitative methods are used to support the results of quantitative calculations. Data was collected based on Instagram content both photos, video viewers, reels, and posters uploaded in the last 1 year period (2025), in collecting engagement rate data not only using the number of likes, comments, shares, and reposts, but also can be taken by measuring how often people click on the link and bio in the post as well as the distribution of questionnaires which will later be processed using SPSS tools. The results of the analysis show that the engagement rate of the content on the Wicida TvInstagram account on average has an average engagement rate that can be said to be quite good. And while users are generally more interested in entertainment content than formal educational content, factors such as upload time and creativity have been shown to influence user engagement levels. This research provides insights for social media account managers in improving content strategies to achieve optimal follower engagement.  
Decision Support System Selection of Achievement Employees in PT PLN (PERSERO) UP3 Samarinda using Simple Multi-Attribute Rating Technique (SMART) Method Ita Arfyanti; Ekawati Yulsilviana; Muhammad Iqbal
TEPIAN Vol. 2 No. 1 (2021): March 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this research is to produce a Decision Support System for Selection of Outstanding Employees at PT PLN (Persero) UP3 Samarinda by using the SMART (Simple Multi Attribute Rating Technique) method with the hope that the selection will be carried out objectively. By using the PHP programming language and the database used is MySQL. In this study, the data collection techniques used were literature study, observation and interviews. The decision support system for selecting outstanding employees at PT PLN (Persero) UP3 Samarinda, is a system designed to assist in making decisions in selecting the right outstanding employees using the help of the SMART method, using the feasibility study system development stage, designing, selecting and making a support system. Decision. The result of this research is the creation of a decision support system to make decisions for high performing employees. Users can input employee data, criteria data and sub criteria data. Then the system will look for a solution using the SMART (Simple Multi Attribute Rating Technique) method. After the decision is obtained, the system will display the decision
Android Based Heart Rate Detection Tools with Arduino Nano Hidayatul Muttaqin; Ita Arfyanti; Wahyuni
TEPIAN Vol. 2 No. 1 (2021): March 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i1.337

Abstract

Android-based Heart Rate Detector Using an Android-Based Fingerprint Using Arduino Nano at Midwife Dwi Inggrini's Maternity Clinic with the hope of helping and simplifying the medical team in checking the heart rate of pregnant women without having to carry devices that are not portable, improving services and errors due to blackouts PLN electricity. The software development method used is the prototype method which includes data collection, design, prototyping, the testing phase by conducting Black Box and White Box testing. To access this tool the user must first connect the bluetooth android device with bluetooth HC-05 on the Arduino device, after the two Bluetooth devices are connected.
Penerapan Algoritma Apriori dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop Renaldi Nur Fahrizal; Ita Arfyanti; Ulfah Nurfadhila
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1056

Abstract

The currently intensive increase in competition within the cafe industry demands that business operators, such as Coffee shop Zecoff Tenggarong, not only focus on product quality but also gain a deep understanding of consumer behavior and buying interest patterns. This understanding is crucial for formulating targeted and sustainable business strategies. This research specifically focuses on analyzing consumer buying interest patterns at Coffee shop Zecoff Tenggarong through the identification of products that tend to be purchased together in a single transaction. To achieve this objective, the study employs a Data Mining approach using the Association Rule Mining technique. The core method implemented on the cafe's sales transaction data over a specific period is the Apriori Algorithm. This algorithm was chosen due to its effectiveness in processing large datasets and identifying frequently co-occurring itemsets. The data analysis process includes the stage of determining critical parameters: support (the frequency degree of the itemset), confidence (the strength of the causal relationship), and lift (the value of association improvement), which are collectively used to filter and generate the strongest and most relevant association rules. The empirical results of the study show that the Apriori Algorithm is highly effective in uncovering hidden purchasing patterns that are difficult to detect through conventional data analysis. The strong association rules derived from this mining process provide important and actionable information for the owner of Zecoff Tenggarong. The strategic implications of these findings include: formulating more targeted cross-selling marketing strategies (for example, recommending companion products that are certainly in demand), optimizing product arrangement (placing strongly associated items in close proximity), and increasing inventory management efficiency (ensuring that items frequently bought together are always in stock). In conclusion, this research concludes that the utilization of Data Mining technology with the Apriori Algorithm is a vital and transformative tool. It not only supports daily operational decision-making but also significantly enhances the coffee shop's competitiveness amidst a tight market rivalry.
Nonlinear Modeling of Agricultural and Environmental SDG Indicators in ASEAN Using Extreme Learning Machine Algorithms Ita Arfyanti; Muhammad Ibnu Sa'ad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7041

Abstract

This study presents a data-driven approach for modeling Sustainable Development Goal (SDG) indicators in ASEAN countries using the Extreme Learning Machine (ELM) algorithm. Focusing on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land), we utilized FAOSTAT datasets from 2020 to 2024 to forecast key indicators such as undernourishment, water use efficiency, and forest area. ELM, known for its rapid learning speed and capability to model nonlinear relationships, outperformed baseline models Linear Regression and Support Vector Machine (SVM) in terms of R² score, RMSE, and MAE. Specifically, ELM achieved R² values exceeding 0.93, with up to 54% RMSE reduction compared to linear models. The model successfully captured national development trends, including deforestation in Indonesia and Cambodia, water stability in Brunei, and varied progress in sustainable agriculture across the region. This study underscores the effectiveness of the Extreme Learning Machine (ELM) in forecasting Sustainable Development Goal (SDG) indicators and provides actionable insights to support evidence based policy planning, particularly in resource-constrained settings. The findings demonstrate that ELM’s combination of interpretability, computational efficiency, and scalability positions it as a highly valuable tool for real-time monitoring of sustainable development across Southeast Asia.
Classification of Diabetes Diseases Based on Medical Features Using Optimized Support Vector Machine Ita Arfyanti; Amelia Yusnita; Pitrasacha Adytia
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8880

Abstract

Diabetes mellitus is a chronic disease caused by impaired glucose metabolism and has become a global health threat with a steadily increasing prevalence each year. According to WHO and IDF, the number of people living with diabetes is projected to reach 783 million by 2045. This condition demands the development of an accurate and efficient early detection system to support medical decision-making. This study aims to develop an optimized Support Vector Machine (SVM)-based classification model to enhance the accuracy and interpretability of diabetes prediction. The dataset used is the Pima Indians Diabetes Dataset, which consists of eight medical features such as glucose level, blood pressure, and body mass index (BMI). The research stages include data preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), parameter optimization with GridSearchCV, and interpretability analysis through SHapley Additive exPlanations (SHAP). The results show that the optimized SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 82%, with a significant improvement in the diabetes class recall value from 0.564 to 0.83 after optimization. The Area Under Curve (AUC) value of 0.871 indicates the model’s effectiveness in distinguishing between positive and negative classes. The SHAP analysis reveals that Glucose, Age, BMI, and Diabetes Pedigree Function are the most influential features in prediction. These findings emphasize that the combination of normalization, balancing, hyperparameter optimization, and interpretability produces a reliable and transparent SVM model. This model has strong potential for implementation in Clinical Decision Support Systems (CDSS) for accurate and explainable early diabetes detection.
Implementasi Sistem QR Code pada Penyimpanan dan Pengelolaan Barang untuk Meningkatkan Efisiensi Inventory di Toko Sembako AL Rahma Alif Putra Zainuldin; Ita Arfyanti; Presa Taruna Oliver

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10058

Abstract

Abstrak - Pengelolaan data persediaan barang (inventory) yang akurat merupakan kebutuhan vital bagi usaha ritel. Toko Sembako AL Rahma saat ini masih menerapkan pencatatan stok secara konvensional, yang mengakibatkan masalah redundansi data, ketidaksesuaian antara stok fisik dan catatan (data discrepancy), serta lambatnya proses pencarian barang. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi inventory berbasis web dengan mengimplementasikan teknologi Quick Response (QR) Code sebagai identifikasi unik barang. Metode pengembangan perangkat lunak yang digunakan adalah Waterfall, meliputi tahapan analisis kebutuhan, desain sistem, implementasi kode, pengujian, dan pemeliharaan. Sistem ini dirancang untuk memfasilitasi pencatatan transaksi barang masuk dan keluar melalui pemindaian QR Code. Pengujian fungsionalitas sistem dilakukan menggunakan metode Black Box Testing untuk memvalidasi kesesuaian input dan output. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berjalan sesuai perancangan dan valid. Implementasi sistem ini terbukti mampu meningkatkan efisiensi waktu pencatatan serta meminimalkan kesalahan manusia (human error) dalam manajemen stok di Toko Sembako AL Rahma.Kata Kunci: Sistem Inventory; Quick Response (QR) Code; Metode Waterfall, Manajemen Stok; Pengujian Blackbox;Abstract - Accurate inventory data management is a vital necessity for retail businesses. Currently, AL Rahma Grocery Store applies conventional stock recording, which results in data redundancy, discrepancies between physical stock and records, and slow item retrieval processes. This study aims to design and build a web-based inventory information system by implementing Quick Response (QR) Code technology for unique item identification. The software development method used is the Waterfall model, which encompasses requirements analysis, system design, code implementation, testing, and maintenance phases. The system is designed to facilitate the recording of incoming and outgoing goods transactions through QR Code scanning. System functionality testing was conducted using the Black Box Testing method to validate the conformity of inputs and outputs. The test results indicate that all system features function according to the design and are valid. The implementation of this system is proven to increase recording time efficiency and minimize human error in stock management at AL Rahma Grocery Store. Keywords: Inventory System; Quick Response (QR) Code; Waterfall Method; Stock Management; Black Box Testing;
Co-Authors Ahmadhani Khoirri Alif Putra Zainuldin Amelia Yusnita Arriyanti, Eka Aryanda, Muhammad Astika Erwin Putranto Azahari Bagus Perwira Utama Yoga Suwatno Bayu Purwanto Bonifasius Candra Bustomi, Tommy Cherly Silia Maretana Dana Sudana Hakim Dendy Kurniawan Dhanny Rindya Pradifta Dicky Rifaldi Didin Rosita Arini Dwi Andrian Nurbawono Edy Purwanto Ekawati Yulsilviana Ekawati, Hanifah Eni Uswatun Hasanah Hanifah Ekawati Harianto, Kusno Harianto, Kusno Haristyawan, Ivan Harpad, Bartolomius Hendri Setiawan Henny Afrida Heny Pratiwi Hidayatul Muttaqin Jihan Nabilah Kresensia, Trisda Kusnandar Kusnandar Lailiyah , Siti Lamsi, Rahmadiansyah Zain Muhammad Fahmi Muhammad Ibnu Sa'ad Muhammad Iqbal Muhammad Rega Praduana Nabila Marsya Zikryanti Raharjo Nur Afifah Nurhuda, Asep Nursobah, Nursobah Pitrasacha Adytia Presa Taruna Oliver Rajiansyah Rajiansyah Rajiansyah, Rajiansyah Renaldi Nur Fahrizal Ryan Agusta Salmon Salmon Salmon Siti Lailiyah Situmorang, Ira Sofia Sony Darmawan Sugeng Suryani Sururi, M Za’iem Suryani, Sugeng Tabrani Rija Tommy Bustomi Tommy Bustomi Ujang Sumarwan Ulfah Nurfadhila Vania Diva Az-zira Vilianty Rafida Wahyuni - Wahyuni Wiwik Widiyatni Yulindawati, Yulindawati Yulindawati, Yulindawati YUNITA Yunita Yunita Yunita Yusika, Andi