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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

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DATA MINING CLUSTERING MENGGUNAKAN ALGORITMA K-MEANS UNTUK KLASTERISASI TINGKAT TRIDARMA PENGAJARAN DOSEN Rizki Muliono; Zulfikar Sembiring
CESS (Journal of Computer Engineering, System and Science) Vol 4, No 2 (2019): JULI 2019
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.271 KB) | DOI: 10.24114/cess.v4i2.13620

Abstract

Universitas medan area memiliki dosen dengan jumlah yang banyak dimana setiap dosen mengampuh matakuliah sesuai bidang keahliannya masing-masing. Setiap semesternya dosen diwajibkan membuat dokumen pengajaran seperti Silabus, RPS, Kontrak Kuliah, RPP dan kemudian diupload ke aplikasi RPS milik Universitas Medan Area untuk dinilai oleh Unit LP2MP yang memiliki tugas malakukan klasterisasi terhadap hasil pembobotan nilai dari tiap-tiap dokumen dari dosen-dosen. Hasil klasterisasi tersebut selanjutnya akan merujuk pada pemberian besaran nilai tunjangan yang di berikan kepada dosen yang membuat dan mengumpulkan dokumen-dokumen pengajaran tesebut. Untuk membantu klasterisasi digunakan algoritma K-Means Clustering adalah salah satu teknik dari data mining dengan metode clustering non hirarki didalam prosesnya berusaha mempartisi data-data yang ada ke dalam bentuk klaster. Penelitian ini diharapkan dapat membantu proses klasterisasi dengan nilai yang mendekati karakteristik menjadi lebih efektif. Ketepatan prediksi yang dilakukan oleh algortima K-means terhadap 15 data mengalami perbedaan ketepatan, hanya sebanyak 53.33% akurasi prediksi bernilai benar.
Centralized and Mapped GIS Web-Based Covid-19 Data Reporting Application with The Waterfall Method: (Case Study: Information Communication Department of North Sumatra Province) Rizki Muliono
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i1.930

Abstract

Covid-19 pandemic cases are currently increasing and expanding throughout the world, especially in Indonesia in the province of North Sumatra. Data on the number of cases spread in the province of North Sumatra which is summarized and published, sometimes there are still disputes over the number and lack of organization in the number of records and their distribution, so there are often errors in the data collected by the health department and which will be published to the public. The case study in this research is the design of an information system that regulates the process of recording, moving, accumulating, and mapping GIS data maps to the publication of Positive case data, Patient Under Surveillance, Polymerase Chain Reaction, and Rapid Test results directly through a web-based covid-19 data reporting application. Sourced from each user of every health facility in each district and city from each sub-district in the province of North Sumatra to support the accuracy of data in decision-making built at the North Sumatra Province Information and Communication Office. The method used in developing the application uses the waterfall method, starting from the needs analysis stage, design, implementation to testing until maintenance. The results of the implementation and testing were carried out using the Blackbox and Whitebox methods. Presentation of GIS web data using google maps has not used a threshold value based on a calculated algorithm, but still uses the determination of crisp values so that the results cannot be said to be relevant as a determinant.
Pemberdayaan kelompok karang taruna dalam kegiatan citizen journalism di Desa Mencirim Kecamatan Kutalimbaru, Kabupaten Deli Serdang Dedi Sahputra; Effiati Juliana Hasibuan; Rizki Muliono
Riau Journal of Empowerment Vol 5 No 3 (2022)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/raje.5.3.187-195

Abstract

The problem of drug abuse and criminal acts that occurred in the village of Mecirim, Kutalimbaru District, Deli Serdang Regency, North Sumatra has involved young people. Community service activities are carried out to empower youth groups by increasing creativity through citizen journalism activities, and to maximize the use of digital technology so that the involvement of youth groups in public accountability through the media is maximized so that they are more empowered in social roles in society. The method used is through, first, training through face-to-face lectures by providing citizen journalism material and making personal blogs. Second, face-to-face guidance in the form of editing citizen journalism products produced by youth groups. Third, evaluate the delivery and loading of citizen journalism products to the mass media. The results achieved from this activity are increased knowledge and understanding and skills of youth groups about citizen journalism and citizen journalism activities carried out by youth groups by sending journalistic works published by online mass media.
PERANCANGAN DAN IMPLEMENTASI APLIKASI SISTEM INFORMASI DOKUMENTASI DAN PELAPORAN DOKUMEN BORANG AKREDITASI PROGRAM STUDI PADA UNIVERSITAS MEDAN AREA PROGRAM PKM DIYA 2019 Juanda Hakim Lubis; Rizki Muliono; Nurul Khairina
Jurnal Informatika Kaputama (JIK) Vol 4 No 1 (2020): Volume 4, Nomor 1, Januari 2020
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v4i1.353

Abstract

Medan Area University (UMA) is one of the oldest private universities in northern Sumatra, which was founded by Haji Agus Salim Siregar in 1983. Especially at Medan Area University the document collection processing model for LKPS-APS is currently still not organized with neat and there is still a loss of LKPS-APS documents, especially when the deadline for LKPS-APS preparation is nearing the upload period. To improve performance in document processing, a system will be built to facilitate lecturers and staff in managing LKPS-APS documents properly and in detail, and reduce the reason lecturers or staff cannot attend meetings in the processing and collection of LKPS-APS document forms that can be submitted into the online system and online discussion per document in the LKPS-APS information system according to 9 standard criteria according to the Instrument Akreditasi Program Studi 4.0 (IAPS 4.0) for Study Program Accreditation.
Implementasi Algoritma C4.5 Untuk Klasifikasi Penentuan Penerima Bantuan Langsung Tunai Di Desa Tanjung Rejo Sri Juwita; Muhathir Muhathir; Rizki Muliono
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 1, No 2 (2022): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v1i2.1474

Abstract

Direct Cash Assistance (BLT) provides money to the poor. This cash assis-tance program was made to help the underprivileged deal with the corona-virus (Covid 19) pandemic. The implementation of the C4.5 algorithm in determining the recipients of Direct Cash Assistance (BLT) was conducted by making classifications or rules according to the data of the old benefi-ciaries, then classifying based on the variables of the husband's and wife's work, home status, and the number of dependents. The results of these classifications or rules were used as a basis if there were future Direct Cash Assistance (BLT) recipients. Through the application of the C4.5 algorithm in determining the exact target of Direct Cash Assistance (BLT), beneficiar-ies could receive assistance quickly and then would be shown in a report that could be downloaded.
Prediksi Jumlah Siswa Baru Menggunakan Single Exponential Smooth (Studi Kasus : SMA Dharmawangsa) Bunaya Arthavia Sitorus; Rizki Muliono
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 2, No 2 (2023): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v2i2.2902

Abstract

Prediction is the process of systematically estimating the most likely event to occur in the future based on past and current information, so that errors (the difference between what happened and what is expected) are minimized. Dharmawangsa High School has the goal of adding/improving school facilities and infrastructure. Therefore, a solution must be found to overcome this problem, one of which is predicting the number of new Dharmawangsa High School students so that the Dharmawangsa High School can predict the addition and reduction of school facilities and infrastructure. Because of this, researchers approached the solution by implementing the Single Exponential Smooth method and designing a system that is useful for predicting the number of new students at Dharmawangsa High School. From the research results, the number of new students in 2024 will be 299 science students and 101 social studies students. The smallest mean squared error (MSE) for the number of new science students was obtained with α=0.3, namely 1723,673 and the smallest MSE for the number of new social studies students with α=0.9, namely 1293,873. The smallest Mean Absolute Percentage Error (MAPE) for the number of new science students was obtained with α=0.6, namely 10.29% with a good description of the method used. Meanwhile, the smallest MAPE on the number of new IPS students was obtained with α=0.8, namely 26.64% with a description of the method used was bad. This study concludes that the Single Exponential Smooth Method can be used to predict the number of new students at Dharmawangsa High School so that the predicted value can be known in the following year. As well as researchers managed to build and design a system to predict the number of new students at Dharmawangsa High School.
Perancangan Sistem Informasi Manajemen dalam Pengelolaan data Kepegawaian di Kantor Dinas Perkebunan Provinsi Sumatera Utara Muhammad Farhan Darkani; Rizki Muliono
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 2, No 1 (2023): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v2i1.1894

Abstract

Penggunaan sistem informasi ini akan memberikan kemudahan bagi Dinas Perkebunan Sumatera Utara dalam memanajemen pegawai terkususnya dalam segi penginputan data pegawai. Sehubung dengan mekanisme penginputan data masih menggunakan metode klasik yaitu dengan menggunakan MS Excel dimana penulis mengganggap masih kurang fleksibel. Oleh karena itu, melalui Kerja Praktek ini penulis berharap dapat merancang sebuah web aplikasi dimana para pegawai bisa dapat menginput data mereka dengan lebih mudah dan fleksibel. Pembuatan sistem ini dimulai dari pengumpulan data, analisis sistem, perancangan sistem dan implementasi.
KLASIFIKASI DAUN TEH SIAP PANEN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ARSITEKTUR MOBILENETV2 Marpaung, Febriady; Khairina, Nurul; Muliono, Rizki; Muhathir, Muhathir; Susilawati, Susilawati
Jurnal Teknoinfo Vol 18, No 1 (2024): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v18i1.3435

Abstract

Penentuan waktu panen daun teh adalah faktor penting dalam industri teh yang secara signifikan mempengaruhi kualitas dan nilai jual produk. Maka dari itu, para petani teh dan produsen perlu memahami waktu yang tepat untuk memetik daun teh untuk menghasilkan teh berkualitas tinggi. Klasifikasi daun teh siap panen dapat menjadi solusi efektif untuk membantu dalam menentukan waktu panen yang optimal. Dalam rangka mencapai tujuan ini, pendekatan digital menjadi semakin penting, di mana pengenalan otomatis daun teh dapat dilakukan dengan cepat dan akurat. Salah satu metode yang digunakan dalam penelitian ini adalah Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Convolutional Neural Network (CNN), khusus dirancang untuk mengolah data dua dimensi seperti gambar. Keunggulan CNN terletak pada kemampuannya memahami dan mengklasifikasi aspek dan objek dalam citra. Salah satu arsitektur model Deep Learning menggunakan CNN yang populer adalah MobileNet. MobileNetV2, sebagai modifikasi MobileNet, memperkenalkan inverted residual blocks dan linear bottleneck. Perbedaan utama dengan MobileNetV1 terletak pada penggunaan bottleneck, yang memungkinkan model mengubah input dari tingkat rendah ke deskriptor tingkat tinggi. MobileNetV2 mampu ekstraksi fitur otomatis dan efisien melalui inverted residual blocks, dan linear bottleneck meningkatkan kapabilitas model dalam mengolah informasi. Dengan pendekatan ini, MobileNetV2 menggunakan CNN sebagai alat efektif untuk tugas-tugas klasifikasi citra, menawarkan kemampuan ekstraksi fitur otomatis dan pengolahan informasi yang efisien dalam pengembangan Deep Learning. CNN telah terbukti efektif dalam tugas klasifikasi gambar, dan MobileNetV2 dikenal karena ringan dan efisien dalam penggunaan sumber daya. Dengan menggunakan metode ini, penelitian ini bertujuan untuk mengklasifikasikan tingkat kematangan daun teh. Hasil training dari enam skenario model yang diuji menunjukkan bahwa tingkat akurasi tertinggi tercapai pada pengujian skenario model 2, yaitu sebesar 100%. Pada pengujian ini, hyperparameter yang optimal termasuk epoch sebanyak 50, input shape RGB Channel sebesar 224x224x3, batch size sejumlah 32, dan optimizer yang digunakan adalah Adam. Pentingnya akurasi terlihat dari hasil pengujian menggunakan data testing, di mana model berhasil mencapai akurasi sebesar 100%. Selain itu, nilai presisi, recall, dan f1-score juga mencapai 100%. Hal ini menunjukkan bahwa model yang dikembangkan mampu mengenali dan mengklasifikasikan daun teh siap panen dengan sangat baik
An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based Y. Syah, Rahmad B.; Muliono, Rizki; Akbar Siregar, Muhammad; Elveny, Marischa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1547-1556

Abstract

Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.
The Impact of k-means on Association Rules Mining Algorithms Performance Hasudungan, Andre; Muliono, Rizki; Khairina, Nurul; Novita, Nanda
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20907

Abstract

Association Rule Mining (ARM) is one of unsupervised learning approach of machine learning. It acts as a data analysis technique that enables the identification of frequent patterns, correlations, associations, and causal structures within certain datasets. This method widely used in numerous studies and practices to explore knowledges and strengthen decision making. However, dealing a large dataset with high number of transactions may become the shortcoming for the ARM algorithms, such as Apriori, FP-Growth, and Eclat. It leads them to face several challenges, including computational complexity, long mining durations, and memory consumption. Hence, this paper proposes k-means clustering to generates several groups of data to handle the issue, then proceed the ARM algorithms for each individual produced cluster. The study used Elbow method and Silhouette Coefficient as the method to determining optimum number of clusters to be used. The result pointed out that k-means-ARM generates a greater number of rules and provides more contextually relevant and significant correlations. In term of Lift Ratio average score, the k-means-ARM shows the greater value rather than non k-means ARM. The k-means-ARM combination is robust; this approach improves the efficiency and scalability of ARM for large datasets and enhances the interpretability of the discovered association rules
Co-Authors -, Muhathir Akbar Siregar, Muhammad Alwi, Buchori Bunaya Arthavia Sitorus Darkani, M. Farhan Dedi Sahputra Effiati Juliana Hasibuan Esrayanti Simanjuntak Fikri, Ridho Ahmad Harahap , Ryanda Fadillah Hasibuan, Samsul Abdul Rahman Sidik Hasudungan, Andre Hidayah, Safrina Ida Royani Irfansyah Iswandi Iswandi Juanda Hakim Lubis Juanda Hakim Lubis Laia, Aldi Irfan Marischa Elveny, Marischa Marpaung, Febriady Merri Hafni Muhammad Farhan Darkani Muhammad Hamdani Santoso Muhathir Muhathir Muhathir Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Nurul Hidayani Nurul Khairina Nurul Khairina Prasetyo , Dewo Putri, Riza Dwi Razaq, Faisal Sandy, Boy Siahaan, Ricardo Fransdoli Siregar, Yusril Izza Haholongan Sitopu, Essay Puspita Sri Juwita Sri Wahyuni Suhaira, Zatin Supriatno, Supriatno Susilawati Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Tino Hermanto Y. Syah, Rahmad B. Yopan Rahmad Aldori Zebua, Meniati Zulfikar Sembiring Zulfikar Sembirirng