Article Per Year (5 Year)
p-Index From 2021 - 2026
4.491
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo Riau Journal of Empowerment Jurnal Informatika Kaputama (JIK) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Anugerah: Jurnal Pengabdian Kepada Masyarakat Bidang Keguruan dan Ilmu Pendidikan INCODING: Journal of Informatics and Computer Science Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Rizki Muliono
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Analysis of Recommendation System on Travel Platform Using Content-Based Filtering and Collaborative Filtering Algorithms at PT. Angkasa Tour & Travel Prasetyo , Dewo; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14429

Abstract

This study aims to evaluate the effectiveness of the recommendation system on the PT Angkasa Tour & Travel travel platform using content-based filtering and collaborative filtering algorithms. The background of the identified problem is the need to improve the accuracy and relevance of recommendations in the travel platform, which functions to assist users in choosing travel services that suit their preferences. This research method includes an analysis of the application of the content-based filtering algorithm that focuses on the characteristics of individual users and products, as well as the collaborative filtering algorithm that utilizes collective user behavior patterns. The results of the study indicate that content-based filtering is effective in providing recommendations based on specific user preferences and product attributes, while collaborative filtering is able to produce recommendations based on collective user behavior patterns. This study also reveals that the combination of the two approaches can improve the accuracy and relevance of recommendations, thus better meeting user needs. The conclusion of this study is that the integration of content-based and collaborative filtering in the recommendation system can provide a more comprehensive solution to meet user preferences and needs on the PT Angkasa Tour & Travel travel platform.
The Implementation of Random Forest to Predict Sales a Case Study at Chatime Binjai Supermall Sandy, Boy; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14431

Abstract

In an increasingly competitive business environment, retail industries like Chatime Binjai Supermall must quickly adapt. Changes in consumer trends, preferences, and technological advancements significantly impact business strategies. To stay competitive, Chatime Binjai Supermall needs to optimize sales, marketing, and inventory management through accurate data analysis and prediction. Random Forest, a powerful machine learning algorithm, is used to process historical data and more accurately predict sales. This study evaluates the performance of Random Forest in predicting daily, weekly, and monthly sales. The analysis shows that products like "Jasmine Green Tea (L)" have the highest daily demand, "PEARL (L)" leads weekly sales, and there is an increase in demand for specific products monthly, such as "CT RAINBOW JELLY (L)." The implementation of the Random Forest algorithm at Chatime Binjai Supermall demonstrates significant potential in enhancing sales efficiency and data-driven decision-making, helping the company remain relevant and competitive amidst market changes.
The Application of Genetic Algorithm in Construction Project Planning System At Cv. Haza Mulia Engineering Harahap , Ryanda Fadillah; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14432

Abstract

Project scheduling is a crucial aspect in construction project management that aims to ensure that all tasks are carried out in an optimal sequence to maximize efficiency and reduce completion time. This study has three main objectives: (1) to build a web-based construction project planning system at CV. Haza Mulia Engineering, (2) to apply genetic algorithms to the construction project planning system at CV. Haza Mulia Engineering, and (3) to analyze the performance of genetic algorithms in generating optimal project schedules. This study was motivated by the need to complete a final assignment or thesis and used genetic algorithms as the main method. The research process begins with the identification of tasks and dependencies in a construction project. An initial population consisting of random schedules is generated and evaluated using a genetic algorithm. The selection, crossover, and mutation processes are carried out to gradually produce a new, better population. The fitness of each individual is calculated based on the number of unconnected activity dependencies, and the algorithm stops when the best mutually continuous schedule is found. The main result of this study is a web-based application built using PHP. This application is able to produce more efficient project scheduling compared to conventional methods. The schedule generated by genetic algorithm shows significant reduction in project completion time by minimizing unmet dependencies. The conclusion of this study confirms that the application of genetic algorithm in web-based project planning scheduling can avoid conflicts between activities and make the schedule more structured.
Penerapan Data Mining Menggunakan Algoritma K-Medoids Dalam Pengelompokan Nasabah Penerima Reward Pada PT Dotri Gadai Jaya Zebua, Meniati; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.709

Abstract

The increasing growth of the financial industry makes companies experience intense competitive pressure. PT Dotri Gadai Jaya (PT DGJ) is a private pawnshop company, facing challenges in maintaining and increasing customer loyalty amidst this competition. One of the strategies used by PT DGJ is to provide rewards to customers based on the number of pawn loan transactions. However, companies experience difficulties in grouping reward recipient customers efficiently and accurately. To overcome this problem, it is necessary to apply data mining using the K-Medoids algorithm. The main objective of this research is to apply the K-Medoids algorithm in grouping reward recipient customers at PT DGJ, knowing the grouping results, and evaluating the results using the Davies Bouldin Index (DBI). The results of the grouping of 1,085 customers are 314 customers who received a 30% reward, 540 customers with a 20% reward and 231 customers with a 10% reward. The cluster evaluation result using DBI is 0.368812, which means the cluster quality value is close to 0 or is quite small. So it can be said that the resulting cluster is quite good.
Analisis Klustering Menggunakan Algoritma DBSCAN untuk Deteksi Anomali dalam Data Transaksi Keuangan Alwi, Buchori; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.827

Abstract

Anomaly detection in financial transaction data is a crucial aspect due to the increasing use of e-money, which raises the risk of suspicious activities such as fraud and money laundering. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster transaction data and identify anomalies based on three main variables: transaction amount, transaction frequency, and final balance. The optimal parameters were determined by evaluating various combinations of epsilon (ε) and minPts values using the Davies-Bouldin Index (DBI) as a clustering quality indicator. The analysis results indicate that the optimal parameters are ε of 0.2727 and minPts of 6, with a DBI score of 1.1753. DBSCAN successfully formed six main clusters and detected 138 data points as noise, indicating potentially abnormal transactions. These findings demonstrate that DBSCAN can effectively distinguish between normal and suspicious data without requiring prior assumptions on the number of clusters, contributing to the development of more accurate and adaptive digital transaction anomaly detection systems.
Perancangan Sistem Informasi Manajemen Dalam Pengelolaan Data Kepegawain Di Kantor Dinas Perkebunan Provinsi Sumatera Utara Darkani, M. Farhan; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 2 (2024): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i2.731

Abstract

The use of this information system will provide convenience for the North Sumatra Plantation Service in managing employees, especially in terms of inputting employee data. In connection with the data input mechanism still using the classic method, namely using MS Excel, which the author considers to be less flexible. Therefore, through this Internship, the author hopes to be able to design a web application where employees can input their data more easily and flexibly. The creation of this system starts from data collection, system analysis, system design and implementation.
Analisis Fungsi Aktivasi pada Algoritma Backpropagation dalam Pengenalan Aksara Batak Toba Esrayanti Simanjuntak; Nurul Khairina; Zulfikar Sembirirng; Rizki Muliono; Muhathir Muhathir
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 8 No. 2 (2023): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v8i2.331

Abstract

Indonesia merupakan salah satu Negara Asia yang memiliki suku dan budaya yang beragam. Suku Batak merupakan suku yang ada di daerah Sumatera Utara. Suku ini terbagi menjadi beberapa jenis berdasarkan wilayahnya. Suku Batak Toba memiliki bahasa daerah yang sangat unik dan sistem tulisan yang berbeda. Aksara Batak Toba sering digunakan dalam upacara keagamaan dan peristiwa penting. Dalam penelitian ini, peneliti akan melakukan studi tentang aksara Batak Toba. Peneliti akan menganalisis Algoritma Backpropagation dalam pengenalan aksara Batak Toba dengan variasi fungsi aktivasi. Data input berupa file citra yang akan melalui tahap preprocessing, diikuti dengan ekstraksi fitur, normalisasi, pelatihan, dan pengujian pola aksara Batak Toba. Pada proses pelatihan dan pengujian pola, peneliti akan menggunakan data latih yang terdiri dari beberapa jenis aksara dan melakukan beberapa kali pengujian dengan jumlah epoch yang bervariasi, yaitu 150, 300, 450, 600, 750, 900, 1050, dan 1200 epoch. Dari hasil pengujian yang dilakukan, diperoleh hasil akurasi tertinggi pada dua jenis fungsi aktivasi, khususnya pada epoch ke-1050. Akurasi pada fungsi aktivasi Sigmoid Bipolar mencapai 80,53% dan pada fungsi aktivasi Sigmoid Biner mencapai 78,95%.
Rancang Bangun Aplikasi Penerjemah Bahasa Indonesia Bahasa Nias Menggunakan Algoritma Levensthein Distance Laia, Aldi Irfan; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 4, No 1 (2024): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v4i1.469

Abstract

In this context, there is a need for the development of a translator application that can translate between Indonesian and Nias languages. The aim of this research is to design and build an Android-based translator application that can translate text from Indonesian to Nias and vice versa.In this study, the Levenshtein Distance algorithm was used as a method to perform the translation process. This algorithm uses the calculation of the distance between two strings to identify the similarities and differences between words in Indonesian and Nias. Thus, the Levenshtein Distance can be used to produce accurate and relevant translation results.The design and development of the translator app was done using the Android platform as the development base. The application is designed to provide an intuitive and easy-to-use user interface, thus allowing users to quickly and efficiently translate texts into Indonesian and Nias languages. During the development process, the translator application was tested to ensure good quality and performance. The test results showed that the translation application was capable of producing accurate translations in accordance with the context of the Indonesian language and the Nias language. In conclusion, the research successfully designed and built an Android-based Indonesian-Nias language translator application using the Levenshtein Distance algorithm. This application is expected to be a useful tool in facilitating communication between Indonesian-speaking users and the Nias language. This research also contributed to the development of Nias-based translator applications that were still limited in previous literature.
JARINGAN SYARAF TIRUAN PENGENALAN POLA HURUF DENGAN JARINGAN HEBB Muliono, Rizki; Lubis, Juanda Hakim
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 2 No. 1 (2018): Volume 2, Nomor 1, Januari 2018
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v2i1.672

Abstract

Pengenalan pola karakter dalam ilmu artifisial intelegen merupakan metode yang sangat bekembang cepat dan terus dikembangkan hingga saat ini, kebutuhan akan keamanan informasi dan sekuritas sebuah informasi ataupun device bisa di block dengan menggunakan sebuah sistem yang tertanam sebuah kemampuan ataupun fitur dalam pengenalan pola. Model ini bisa diterapkan baik pada pegenalan pola wajah, pola sidik jari. Dalam artikel ini akan di bahas tentang analisi pengenalan pola sederhana dengan menggunakan jaringan syaraf tiruan yaitu metode hebbian. Dengan dua pola awal sebagai pengetahuan atau learningbase nya dan kemudian diuji dengan pola – pola inputan baru yang akan di cek kemiripannya dengan kedua pola learning base apakah di kenali sebagai pola satu atau sebagai pola dua. Dengan menggunakan 25 variabel input dan bias 1 dengan nilai bobot awal 0, pola U dan S di inisialisasi dengan karakter x bernilai 1 dan karakter o bernilai -1 dengan output bipolar, U tareget 1 dan S target -1, fungsi f(net) adalah 1 jika Y >= 0 dan -1 jika Y < 0. Jaringan yang telah terbentuk dari dua pola awal yang telah di kenali kemudian weigh jaringan di test dengan pola 6, 3, U’ dan 8 maka di dapat hasil adalah pola 6 dan 3 dikenali atau mendekati mirip S dan U’ mendekati U.
Penerapan Algoritma K-Means Untuk Klasterisasi Pasien Berdasarkan Riwayat Kesehatan dan Jenis Layanan Kesehatan Riza Dwi Putri; Rizki Muliono
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.973

Abstract

The digital transformation in the healthcare sector has led to the generation of large and complex datasets, requiring appropriate analytical techniques to extract meaningful information. This study aims to implement the K-Means algorithm to cluster patients based on their health history and the types of healthcare services they use, in order to support data-driven decision-making in hospital management. The dataset consists of 1,459 patient records from Sapta Medika Hospital, covering attributes such as age, gender, chronic disease history (diabetes, hypertension, heart disease), visit frequency, medical costs, and healthcare service types including outpatient, inpatient, emergency (ER), and telemedicine. The research stages involved data preprocessing, transformation, categorical data encoding, numerical data normalization, and clustering using the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method, which identified K = 3. The clustering results revealed three distinct patient groups: chronic patients with high treatment costs and frequent inpatient services, routine patients with stable conditions mostly using outpatient services, and general patients, usually younger with mild conditions. Principal Component Analysis (PCA) was used to visualize the cluster separation, while the clustering quality was evaluated with a Silhouette Score of 0.47. These results conclude that the K-Means algorithm is effective in producing meaningful and practical patient segmentation, which can be used to design more adaptive, efficient, and patient-centered healthcare service strategies.
Co-Authors -, Muhathir Alwi, Buchori Bunaya Arthavia Sitorus Darkani, M. Farhan Dedi Sahputra Effiati Juliana Hasibuan Esrayanti Simanjuntak Faisal Razaq Fikri, Ridho Ahmad Harahap , Ryanda Fadillah Hasudungan, Andre Ida Royani Irfansyah Iswandi Iswandi Juanda Hakim Lubis Juanda Hakim Lubis Laia, Aldi Irfan Marischa Elveny, Marischa Marpaung, Febriady Merri Hafni Muhammad Akbar Siregar Muhammad Farhan Darkani Muhammad Hamdani Santoso Muhathir Muhathir Muhathir Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Nurul Hidayani Nurul Khairina Prasetyo , Dewo Riza Dwi Putri Safrina Hidayah Samsul A Rahman Sidik Hasibuan Sandy, Boy Siahaan, Ricardo Fransdoli Siregar, Yusril Izza Haholongan Sitopu, Essay Puspita Sri Juwita Sri Wahyuni Supriatno, Supriatno Susilawati Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Tino Hermanto Y. Syah, Rahmad B. Yopan Rahmad Aldori Zatin Suhaira Zebua, Meniati Zulfikar Sembiring Zulfikar Sembirirng