Article Per Year (5 Year)
p-Index From 2021 - 2026
3.035
P-Index
This Author published in this journals
All Journal Jurnal Informatika Universitas Pamulang Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Sistem Informasi dan Sistem Komputer JAMAIKA: Jurnal Abdi Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Inspiration: Jurnal Teknologi Informasi dan Komunikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains Journal of Innovative and Creativity Riau Jurnal Teknik Informatika Indonesian Journal of Infomatics
Kecitaan Harefa
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Perancangan Sistem Informasi Permohonan Penomoran Call Center Berbasis Web Menggunakan Metode Extreme Programming Kiki Nurdiansyah; Kecitaan Harefa
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 01 (2023): OKTAL : Jurnal Ilmu Komputer dan Sains
Publisher : CV. Multi Kreasi Media

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

Abstract

Today’s rapidly growing global telecommunications system has brought people into the world of information and communication technology (information society). The demand for improving public services that are good and satisfying the community is a need that must be met by the government. The purpose of this research is to develop a licensing and application information system and to establish a web-based Call Center numbering at the Telecommunications Directorate of the Ministry of Coommunication and Information Technology in order to provide better services. Developed using the Extreme Programming method and structured analysis modelling, this system was built using the PHP and MySQL programming language. The results of this study are the system can make it easier for applicants to get information, both the availability of numbers, submitting a call center application, numbering reports and monitoring the status of the application file. In addition employees can manage application data submitted by applicants quikly and transparently.
Sistem Pendukung Keputusan Analisa Kelayakan Pemberian Pinjaman Pada Nasabah Koperasi Kemuning Mitra Persada Dengan Metode TOPSIS Indriyani Octavia; Kecitaan Harefa
LOGIC : Jurnal Ilmu Komputer dan Pendidikan Vol. 1 No. 6 (2023): Logic : Jurnal Ilmu Komputer dan Pendidikan
Publisher : Shofanah Media Berkah

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

Abstract

Koperasi Kemuning Persada berpusat di Sawangan Kota Depok merupakan salah satu Lembaga Keuangan Non-Bank yang melayani jasa peminjaman dana kepada nasabahnya dengan jaminan berupa Buku Pemilik Kendaran Bermotor atau Mobil (BPKB). Dalam pemberian pinjaman dana kepada calon nasabahnya harus dengan persetujuan dari kepala koperasi yang bisa menyebabkan pekerjaan kurang efektif dan objektif dalam pelaksanaannya, karena akan menyita banyak waktu serta dapat menimbulkan keputusan yang kurang tepat dan dapat berpotensi adanya kredit macet pada nasabah. Pada penelitian ini akan merancang suatu sistem yang dapat memberikan hasil penelitian yang objektif pada setiap calon nasabah dengan tetap mempertimbangkan semua kriteria penilaian, salah satu metode yang dapat digunakan adalah dengan metode TOPSIS.  Metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) didasarkan pada konsep dimana alternatif terpilih yang terbaik tidak hanya memiliki jarak terpendek dari solusi ideal positif, namun juga memiliki jarak terpanjang dari solusi ideal negatif. Berdasarkan implementasi dan pengujian yang telah dilakukan, hasil penelitian menunjukan bahwa dengan adanya sistem pendukung keputusan kelayakan nasabah berbasis web dapat memberi kemudahan dalam menentukan kelayakan calon nasabah sehingga dapat membantu koperasi dalam proses pinjaman terhadap nasabah yang dipilih.
Perancangan Sistem Informasi Pendaftaran Pasien Rawat Jalan Berbasis Web Menggunakan Metode Waterfall Pada Klinik Pratama Yakrija Arif Cahyo Utomo; Kecitaan Harefa
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 1 No. 5 (2023): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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

Abstract

At the Yakrija Pratama Clinic, it was found that patients who came had to register directly and in the data written in the patient registration book. The data collection process by writing the patient's identity by the registration officer is carried out one by one resulting in a queue. In addition, written data collection can slow down the performance of registration officers because they have to look for one by one processing the patient's medical record data that has been collected previously. Data storage in the form of documents also has the potential for data to be lost, tucked away or vulnerable to damage. And of course data collection in writing in the patient registration book resulted in making reports take quite a long time. From the problems above, the author makes a web-based outpatient registration information system application using the waterfall method as a system development method. This system is designed using the PHP and MYSQL programming languages ​​as database servers.
Kecocokan Keputusan Pohon Algoritma pada Kimia Organik: Perbandingan ROC AUC Keputusan Pohon dan Ketetanggaan Muhammad Zirlda Prairi; Zurnan Alfian; Kecitaan Harefa
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.1649

Abstract

Kehadiran Machine Learning (ML) di ruang lingkup komputasi modern telah menyebabkan banyak permasalahan dan solusi, terutama pembahasan algoritma. Penelitian ini menganalisa dan eksplorasi efektifitas lima algoritma ML dalam mengklasifikasi asam amino lazim esensial pada protein berdasarkan strukur molekul dan jumlah atom. Dataset diambil secara manual dari buku kimia organik klasik, yang dikonversi dari rumus dan gambar struktur senyawa asam amino lazim menjadi fitur numerik seperti jumlah atom karbon, hidrogen, nitrogen, oksigen, dan sulfur. Kelima algoritma ML yang dianalisis yakni Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbour (KNN), K-Means, dan Random Forest. Setiap algoritma dilakukan evaluasi menggunakan nilai akurasi, precision, recall, F1-score, serta Area Under the Curve (AUC) dari kurva Receiver Operating Characteristic (ROC). Hasil menunjukkan bahwa algoritma Decision Tree, Random Forest, dan KNN memiliki tingkat akurasi terbaik dengan skor AUC sebesar 0,83. Studi kasus penelitian ini menawarkan pendekatan pengubahan data mentah menjadi fitur yang bisa dimengerti oleh algoritma ML dan klasifikasi biner dengan label nol dan satu pada dataset berskala kecil. Penelitian berikutnya disarankan menggunakan dataset yang lebih besar dan menerapkan validasi silang untuk meningkatkan generalisasi model.
Comparison of Random Forest and K-Nearest Neighbor Algorithms for Prediction of Customer Credit Risk Based on Transaction History and Income Kecitaan Harefa
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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

Abstract

The increase in the number of credit applications requires financial institutions to have an accurate risk assessment system in order to minimize the potential for bad loans. The main problem that often arises is the difficulty in objectively assessing the level of customer risk based on varied transaction history and income data. To overcome this, this study compared two classification algorithms in machine learning, namely Random Forest and K-Nearest Neighbor (KNN), to predict customer credit risk. The goal is to determine the algorithm that provides the best level of prediction accuracy and stability. The research method includes data collection from the credit risk dataset, the pre-processing stage (data cleaning, encoding, and normalization), the separation of the data into training and testing sets, then model training using both algorithms. Evaluations were conducted based on accuracy, precision, recall, F1-score, and ROC-AUC metrics. The test results showed that the Random Forest algorithm provided superior performance to KNN, with an accuracy rate of around 90%, while KNN only reached around 84%. This shows that Random Forest is more effective at handling data with complex and non-linear variables. In conclusion, the use of the Random Forest method can be the optimal solution for financial institutions in identifying customers' credit risks more accurately and efficiently.
An Adaptive Computational Model for Detecting Concept Drift in Long Term Data Streams Using Incremental Learning Approaches Rinna Rachmatika; Kecitaan Harefa
Indonesian Journal of Infomatics Vol. 1 No. 1 (2026): February: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i1.28

Abstract

Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using incremental learning techniques and statistical drift detection mechanisms. The model integrates an Adaptive Drift Detector (ADD) and Incremental Learning System, enabling real-time adjustments to data distribution changes. The model is evaluated across synthetic and real-world datasets, demonstrating its superior ability to detect abrupt, gradual, and recurring drifts compared to traditional models. Experimental results indicate that the adaptive model maintains high prediction accuracy, minimizes false positive rates, and reduces detection delays. Furthermore, the model performs well in resource-constrained environments, making it suitable for real-time applications such as healthcare prediction, fault detection, and IoT systems. Despite its promising performance, the study identifies challenges related to computational complexity and the model’s performance with imbalanced datasets and noisy data. Future research should focus on optimizing the model’s scalability, computational efficiency, and adaptability to more complex data types to ensure broader applicability in dynamic environments. This work contributes to advancing the detection and adaptation of concept drift, offering a robust solution for dynamic and evolving data streams.
Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Risiko Stunting Balita Berbasis Web di Posyandu Belimbing Andrian Andrian; Kecitaan Harefa
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1247

Abstract

Chronic nutritional deficiency issues such as stunting demand early monitoring accompanied by precise data documentation. Field practices indicate that Posyandu Belimbing still relies on conventional recording mechanisms to monitor toddlers' growth and development. This triggers risks of delayed intervention, data entry errors, and interpretation bias. As a solution, this research designs a web-based information system to classify the threat level of stunting through the application of the K-Nearest Neighbor (KNN) algorithm. The five metric indicators analyzed include the toddler's age, body weight, height, head circumference, and upper arm circumference. The classification mechanism relies on Euclidean Distance calculation by setting the nearest neighbor value k=3, which divides the output into low, medium, and high-risk zones. The software development cycle adopts the Agile framework, rolling from the initiation phase to deployment. System evaluation is proven through Black Box testing, White Box testing, and the distribution of questionnaire instruments to 15 field cadres. The functionality testing results confirm that all features operate without bugs, while the user satisfaction index reaches 82.44% (strongly agree category). In conclusion, this digital platform is highly capable of facilitating cadres to detect stunting vulnerability swiftly and in an organized manner.
Implementasi Metode Smart untuk Rekomendasi Pelanggan Memilih Motor Bekas Berbasis Web Pada Cv.Mulana Motor Muhammad Rizki Yusnadi; Kecitaan Harefa
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1260

Abstract

Selecting a used motorcycle is often a complex process, involving numerous criteria such as purchase price, completeness of documents, motorcycle type, year of manufacture, and tax status. At CV. Maulana Motor, the selection process is still manual, making it slow, subjective, and inefficient. This research aims to develop a web application based on a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method to systematically recommend used motorcycles. This application utilizes alternative motorcycle data and assessment criteria, then applies the SMART method through weight normalization, utility value calculation, and final score determination to produce a ranking of the best motorcycles. The development process employed the Waterfall methodology, encompassing requirements analysis, database and interface design, development, black box testing, review, and implementation. Test results demonstrated that the application performed as required, replacing manual processes, increasing efficiency, and facilitating objective decision-making. The system enables fast and structured processing, calculation, and presentation of information. The PCX 160 ABS motorcycle was selected as the best alternative, demonstrating the system's ability to assist customers in selecting the right used motorcycle and supporting administrators in sustainable data management. This system is expected to become a reference model for digitalizing the selection of used motorbikes in other showrooms.
Implementasi Teknologi Gamifikasi Pada Platform Penugasan Dengan Design Thinking di SMP PGRI 8 Bogor Abdullah Abdullah; Kecitaan Harefa
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 2 (2026): Vol 11 No 2 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i2.1361

Abstract

Rendahnya motivasi belajar siswa di SMP PGRI 8 Kota Bogor dipicu oleh sistem penugasan konvensional berbasis kertas yang monoton, sekaligus membebani guru secara administratif. Penelitian ini bertujuan merancang platform penugasan berbasis web dengan integrasi teknologi gamifikasi. Metodologi yang digunakan adalah pendekatan hibrida, menggabungkan Design Thinking untuk analisis kebutuhan berpusat pada pengguna dan Agile Scrum untuk pengembangan sistem. Platform ini mengimplementasikan elemen poin, lencana, level, dan papan peringkat. Hasil pengujian Black-Box menunjukkan fungsionalitas sistem berjalan valid secara keseluruhan. Implementasi gamifikasi terbukti efektif meningkatkan motivasi akademik siswa dengan skor indeks 83,15% (Kategori Sangat Kuat) dan mengefisienkan proses rekapitulasi nilai bagi pendidik.
Implementasi Metode Simpleks dengan Python untuk Optimalisasi Distribusi Barang Kecitaan Harefa
Riau Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Juli 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i2.3410

Abstract

Distribusi barang merupakan salah satu aspek vital dalam rantai pasok perusahaan, di mana efisiensi distribusi dapat berdampak langsung terhadap biaya operasional. PT Acel Furniture sebagai perusahaan yang bergerak dalam bidang distribusi kebutuhan pokok menghadapi permasalahan dalam menentukan pola distribusi barang dari gudang pusat ke beberapa cabang yang efisien secara biaya. Masalah utama yang dihadapi adalah tingginya biaya distribusi akibat pembagian barang yang tidak optimal. Oleh karena itu, diperlukan suatu pendekatan matematis untuk menentukan alokasi distribusi yang meminimalkan total biaya pengiriman. Penelitian ini bertujuan untuk mengoptimalkan distribusi barang pada PT Acel Furniture dengan memformulasikan masalah tersebut sebagai persoalan linear programming. Metode yang digunakan adalah metode simpleks, yang kemudian diimplementasikan menggunakan bahasa pemrograman Python dengan pustaka scipy.optimize. Studi kasus dilakukan pada satu gudang pusat dan tiga cabang distribusi, dengan data permintaan masing-masing cabang dan biaya pengiriman per unit yang berbeda. Hasil penelitian menunjukkan bahwa metode simpleks mampu memberikan solusi distribusi optimal yang memenuhi seluruh permintaan cabang tanpa melebihi kapasitas gudang. Total biaya distribusi yang dihasilkan dari solusi optimal adalah sebesar Rp880.000, yang dihitung berdasarkan alokasi pengiriman 100 unit ke Cabang A, 120 unit ke Cabang B, dan 80 unit ke Cabang C. Implementasi metode simpleks dengan Python terbukti efektif dan efisien untuk membantu pengambilan keputusan dalam proses distribusi barang. Penelitian ini diharapkan dapat menjadi referensi bagi perusahaan dalam mengoptimalkan logistik distribusi secara kuantitatif dan berbasis teknologi.
Co-Authors Abdullah Abdullah Aenun Nisa Andrian Andrian Ardianto Moenir Arif Cahyo Utomo Cicih Sri Asih Daevi Dandi Ramdani Fikri Miftahul Falah Indrajit Sumarsono Indriyani Octavia Joko Priambodo Kiki Nurdiansyah M. Chandra Nugraha Muhammad Rizki Yusnadi Muhammad Zirlda Prairi Ny.Ayni Suwarni Herry Perani Rosyani Rinna Rachmatika Zurnan Alfian