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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal TIMES Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Informasi MURA Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Science, Engineering, Technology, and Education U-NET Jurnal Teknik Informatika Jurnal Teknik Informatika (JUTIF) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) PUBLIDIMAS CSRID SISFOTENIKA
Rika Rosnelly, Rika
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PENGENALAN POLA KEMAMPUAN PELANGGAN DALAM MEMBAYAR AIR PDAM MENGGUNAKAN ALGORITMA NAÏVE BAYES Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul; Batubara, Ela Roza; Alkhairi, Putrama; Tambunan, Fazli Nugraha; Rosnelly, Rika
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (828.686 KB) | DOI: 10.51351/jtm.10.2.2021656

Abstract

Dengan meningkatnya jumlah MBR (Masyarakat Berpenghasilan Rendah) yang masuk setiap tahunnya dimasing-masing wilayah di Pematansgsiantar, pihak PDAM Tirta Lihou berencana mencari alternatif solusi dalam menangani permasalahan kemampuan pelanggan dalam membayar tagihan air sehingga biaya opersional tetap bisa berjalan baik dan produksi dapat memenuhi kebutuhan masyarakat. Dalam menentukan alternatif untuk menentukan kemampauan masyarakat dalam membayar tagiahan air digunakan metode datamining. Dengan menggunakan teknik datamining khususnya klasifikasi menggunakan algoritma Naive Bayes dapat dilakukan prediksi terhadap kemampauan pelanggan dalam membayar tagihan air bersih berdasarkan data yang ada. Naive bayes adalah teknik prediksi probabilistik sederhana yang berdasarkan pada teorema Bayes dengan asumsi independensi (ketidak tergantungan) yang kuat. Berdasarkan hasil dari perhitungan menggunakan algoritma Naive Bayes, diperoleh hasil klasifikasi dari 30 alternatif yang digunakan, dimana terdapat 11 kelas mampu membayar tagihan dan 19 Tidak Mampu dengan total Accuracy yang diperoleh sebesar 70%. Dari hasil yang diperoleh,diharapkan penelitian ini dapat membantu pihak PDAM Tirta Lihou dalam menentukan lokasi yang layak dilakukan penaybungan sumber air untuk pelanggan yang memiliki prosfek baik dengan kemampuan untuk membayar tagihan air, sehingga dapat meminimalisir kerugian PDAM dan dapat memenuhi kebutuhan masyarakat. Penelitian ini juga diharapkan dapat menjadi referensi bagi peneliti selanjutnya yang berkaitan dengan pengguna algoritma yang digunakan.
EVALUASI DENSENET-201 UNTUK IDENTIFIKASI BIJI KOPI MENGGUNAKAN HYPERPARAMETER GRIDSEARCH Manza, Yuke; Rambe, Lima Hartimar; Siregar, Kiki Putri Ani; Rosnelly, Rika; Setiawan, Adil
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3898

Abstract

Abstract: Coffee is one of the most important commodities in the global agricultural sector. However, the manual sorting process of coffee beans, which is still widely applied in the Small and Medium Industry (IKM) sector, tends to be time-consuming and often results in inconsistent quality assessments. This study aims to classify coffee bean quality using the DenseNet-201 deep learning architecture, optimized with the GridSearch method to obtain the best combination of hyperparameters. The dataset used consists of 450 images of coffee beans divided into two classes: good-quality and defective beans. The model was trained for 20 epochs using a transfer learning approach and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The test results show that the model before optimization achieved an accuracy of only 78.67%, while the model optimized with GridSearch reached a high accuracy of 99.47% with a low loss value. These findings indicate that the application of DenseNet-201 with hyperparameter tuning is capable of producing accurate and stable classification results, and can be relied upon as an automated solution for sorting coffee beans based on their quality. Keywords: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Coffee Bean Classification Abstrak: Kopi merupakan salah satu komoditas penting dalam sektor pertanian global. Namun, proses pemilahan biji kopi secara manual yang masih banyak diterapkan pada sektor Industri Kecil dan Menengah (IKM) cenderung memakan waktu dan menghasilkan penilaian kualitas yang tidak konsisten. Penelitian ini bertujuan untuk mengklasifikasikan kualitas biji kopi menggunakan arsitektur Deep Learning DenseNet-201 yang dioptimalkan dengan metode GridSearch untuk memperoleh kombinasi hyperparameter terbaik. Dataset yang digunakan terdiri dari 450 gambar biji kopi dengan dua kelas: biji kopi bagus dan biji kopi rusak. Model dilatih selama 20 epoch dengan pendekatan transfer learning dan dilakukan evaluasi terhadap performa model menggunakan metrik akurasi, precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa model sebelum optimasi hanya mencapai akurasi sebesar 78,67%, sedangkan model dengan optimasi GridSearch mampu mencapai akurasi tinggi sebesar 99,47% dan nilai loss yang rendah. Hal ini menunjukkan bahwa penerapan DenseNet-201 dengan tuning hyperparameter mampu menghasilkan klasifikasi yang akurat dan stabil, serta dapat diandalkan sebagai solusi otomatis dalam proses sortasi biji kopi berdasarkan kualitasnya. Kata kunci: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Klasifikasi Biji Kopi
Implementasi Teknologi Informasi untuk Meningkatkan Strategi Penjualan pada UKM Driply Coffee Berbasis Online Wahyuni, Linda; Hardianto, Hardianto; Rosnelly, Rika; Syahrian, Achmad; Rahmadi, Diky
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 1 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol5No1.pp7-12

Abstract

Small and medium enterprises (SMEs) are similar to the category of Small and medium enterprises (SMEs), but are usually used to refer to small and medium enterprises only, without including micro businesses. Community service carried out at UKM Driply Coffee, which provides types of drinks in the form of coffee and several other types of drinks besides, also providing food that can accompany customers when they come and sit down to relax either alone, with friends, colleagues, or with family. The research conducted looked at the process of its marketing system, which uses social media such as Facebook, Instagram, and also WhatsApp. The system is indeed seen by many customers, only the process for ordering and selecting menus and the payment process cannot all be seen in the display due to the limitations of the image display, from the presentation of these conditions this research was conducted for approximately 3 months, and will be designed and implemented an online-based application that uses a web program and a MySQL database. Customers can access remotely and can also make payments online, and delivery of drinks or food will be carried out according to the intended location. The convenience felt is not only for customers, but partners will also get convenience and be able to compete in the world of small and medium enterprises (UKM).
Pengenalan Pola Aksara Batak menggunakan Backpropagation Sumantri, Ekoliyono Wahyu; Nasution, Ammar Yasir; Suyono, Suyono; Rosnelly, Rika
SISFOTENIKA Vol. 14 No. 1 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i1.419

Abstract

Pengenalan pola adalah proses pengidentifikasian, pemodelan, dan pengklasifikasian pola dalam data. Tujuannya adalah untuk menemukan hubungan atau struktur dalam data yang dapat digunakan untuk memahami, mengklasifikasikan, atau membuat prediksi tentang data yang baru atau tidak terlabel. Ada berbagai jenis teknik dan pendekatan yang digunakan dalam pengenalan pola, termasuk metode statistik, metode pembelajaran mesin, dan jaringan saraf tiruan. Beberapa pendekatan umum dalam pengenalan pola: Jaringan saraf tiruan (artificial neural network) adalah model matematika yang terinspirasi oleh cara kerja otak manusia. Jaringan ini terdiri dari banyak neuron yang terhubung dalam lapisan-lapisan dan dapat belajar melalui proses pelatihan dengan algoritma backpropagation. Jaringan saraf tiruan dapat digunakan untuk mengenali pola dalam data yang kompleks dan menemukan hubungan non-linear antara fitur dan keluaran. Penting untuk dicatat bahwa setiap tugas pengenalan pola memiliki karakteristik dan kebutuhan yang berbeda, dan pendekatan yang tepat dapat bervariasi. Jaringan saraf tiruan memiliki keunggulan dalam kemampuan mereka untuk menangani pola-pola kompleks dan non-linear dalam data. Dalam prakteknya, arsitektur jaringan, fungsi aktivasi, algoritma pembelajaran, dan parameter lainnya harus disesuaikan dengan tugas pengenalan pola yang spesifik. Jaringan saraf tiruan dapat digunakan untuk mengenali pola dalam data. Pengenalan pola adalah tugas yang umum dipecahkan menggunakan jaringan saraf tiruan. Sehingga pada pembahasan ini data – data yang di extrak dari gambar aksara batak akan diubah menjadi pola, sehingga memberikan pendekatan kepada pengolahan data secara kwantitatif dimana, gambar gambar yang telah di tentukan akan di ubah menjadi gambar bentuk bipolar atau bilangan 1 dan -1, karena data bipolar yang telah disesuaikan pada data masukan dan data keluaran yang di sesuaikan dengan target yang telah ditentukan. Dan setelah di lakukan pengujian dengan menggunakan metode Bacpropagation, pola yang ada pada gambar aksara batak di kenali dengan baik dimana hasil pengenalan sampai 97 % dari data gambar yang telah di tentukan.
Unity Engine Sebagai Media Pengembangan Game Musik Cross Platform Ramadhan, Muhammad Yakub; Rosnelly, Rika
U-NET Jurnal Teknik Informatika Vol. 7 No. 2 (2023): U-NET Jurnal Teknik Informatika | Agustus
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v7i2.560

Abstract

Majority of game developers only release their games for mainstream and well known platforms like Windows because of their huge user base which makes any other platforms like Linux can’t enjoy some new games only Windows users have. But with vast improvements in game development scene now a game developer can release a cross platform game with Unity Game Engine that supports many platforms including Linux. This research is conducted to develop an Unity cross platform game with rhythm game as the game genre which can be played with less demanding hardware and also taking part in improving players cognitive function.
Pengenalan Jenis Hama Pada Daun Kelapa Untuk Penentuan Pertumbuhan Kelapa Berdasarkan Citra Digital Sukriatna; Rosnelly, Rika
U-NET Jurnal Teknik Informatika Vol. 8 No. 1 (2024): U-NET Jurnal Teknik Informatika | Februari
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v8i1.1073

Abstract

Petani merupakan seseorang yang memelihara tanaman seperti kelapa. Setiap petani berhak menerima hasil terbaik dari yang di tanam. Lahan yang bagus mampu menghasilkan tanaman yang baik, kualitas tanaman terbaik di hasilkan ketika pertumbuhan tanaman stabil. Pertumbuhan tanaman stabil dilakukan dengan cara mengontrol pertumbuhan pada tanaman. Pada umumnya petani mengontrol pertumbuhan tanaman dengan cara tradisional, hal tersebut membuat kekeliruan petani untuk pertumbuhan tanaman. Namun seiring dengan perkembangan teknologi pengawasan pertumbuhan pada tanaman dapat dilakukan secara terkomputerisasi. Dengan adanya permasalahan tersebut maka diperlukan sebuah sistem untuk mengenali jenis hama pada daun kelapa. Pengenalan jenis hama tersebut dapat diterapkan dan diaplikasikan dengan menggunakan metode K-Means Clustering dan SVM (Support Vector Machine). K-Means Clustering adalah metode yang digunakan untuk segmentasi citra sedangkan SVM adalah metode yang digunakan untuk pengakelasan data jenis hama.
Challenges and Strategies in Forensic Investigation: Leveraging Technology for Digital Security Using Log/Event Analysis Method Nasution, Ammar Yasir; Hartono, Hartono; Rosnelly, Rika
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.42815

Abstract

Cybersecurity threats continue to evolve, necessitating advanced techniques for network anomaly detection. This study developed a comprehensive methodology for detecting network anomalies by leveraging sophisticated log and event analysis using machine learning algorithms. By employing a Naive Bayes classification approach on a synthetic cybersecurity dataset comprising 40,000 entries with 25 unique features, the research aimed to enhance anomaly detection precision. The methodology involved meticulous data preprocessing, feature selection, and strategic model validation techniques, including cross-validation and external benchmarking. Comparative analysis with K-Nearest Neighbors and Support Vector Machine algorithms demonstrated the Naive Bayes method's superior performance, achieving a classification accuracy of 94.8%, an Area Under the Curve (AUC) of 0.949, and a Matthews Correlation Coefficient of 0.896. The study identified critical parameters influencing anomaly detection, such as source port characteristics and attack signatures. These findings contribute significant insights into machine learning-based network security strategies, offering a robust framework for early threat identification and mitigation.
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Gea, Muhammad Nasri; Wanayumini, Wanayumini; Rosnelly, Rika
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
A Comparative Analysis on the Evaluation of KNN and SVM Algorithms in the Classification of Diabetes Limas, Agus Fahmi; Rosnelly, Rika; Hartono, Hartono; Nursie, Aly
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.44269

Abstract

Purpose: Diabetes has received a great deal of attention in medical research because of its profound effect on human health. Many factors cause this disease in the human body. Can be from food or drink that is often consumed by the human body. Diabetes cannot be cured and can only be controlled.Methods: In this study, using 2 data mining techniques namely Support Vector Machine and K-Nearest Neighbor were applied to predict diabetes. In this study, 768 diabetes data were used as trial data, consisting of training data that had been pre-processed data and 400 data cleaning data, 278 data testing data, and 50 diabetes data samples used as samples in the calculation.Result: The performance of each algorithm is analyzed differently, the results of each best algorithm will be analyzed to determine which algorithm can provide better results for predicting diabetes. The results obtained in this study get a value of 0 where the predicted value of the target class for new data is the negative class (Suffer).Novelty: This study compares the SVM and K-NN methods for diabetes classification. So, successfully implemented for data on the classification target
Decision Support System Application Evaluation of Transformer Isolation Condition with Simple Additive Weighting (SAW) Method Rosnelly, Rika; Gunawan, Teddy; Paramitha, Cindy; Sadikin, Muhammad
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 1 No 1 (2020): APRIL 2020
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/abdimastek.v1i1.914

Abstract

The use of computer technology has spread among workers and companies. Therefore researchers recommend a system that can overcome the problem of assessing the conditions of transformer insulation at PT. Electricity System Cemerlang uses a computer system. The system that researchers use is a decision support system. Decision Support System or often called Decision Support System (DSS) is a model-based system that consists of procedures in data processing and consideration to assist managers in making decisions. In order to succeed in achieving its objectives, the system must be simple, robust, easy to control, easily adaptable to important things and easy to communicate with. Implicitly also means that this system must be computer-based and used as an addition to someone's problem solving capabilities. But to be able to use a decision support system properly, a method or an appropriate method is needed to get the right results. Therefore researchers recommend the method of Simple Additive Weighting (SAW). Simple Additive Weighting (SAW) method is often also known as the weighted sum method. The basic concept of the method of Simple Additive Weighting (SAW) is to find a weighted sum of performance ratings on each alternative on all attributes.
Co-Authors Agung Rizky, Muhammad Dipo Agus Fahmi Limas Ptr Aji, Eko Setyo Budi Putra Akbar, Muhammad Barkah Alkhairi, Putrama Ammar Yasir Nasution Amrullah Amrullah Ashari, Annisa Batubara, Ela Roza Bob Subhan Riza, Bob Subhan Chairul Rizal Daifiria Dian Maya Sari ElisaBeth S, Noprita ElisaBeth S Fahriyani, Tasya Finis Hermanto Laia Gea, Muhammad Nasri Habib Satria Habib, Nurhayati Harahap, Charles Bronson Harahap, Sarwedi HARDIANTO - Hartono Hartono Hartono Hartono Haryanto S., Edy Victor Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Kelana Jaya Junaidi Junaidi Kelvin Leonardi Kohsasih Khairi, Ibni Krismona, Lumi Limas, Agus Fahmi Lubis, Dela Aventi Oktavia Br Manza, Yuke Margolang, Khairul Fadhli MARIA BINTANG Mega Christin Morys Lase Mhd Furqan Mochammad Imron Awalludin Muhammad Sadikin Mulkan Azhari Nasution, M. Irfan Aldy Naswar, Alvinur Nursie, Aly Paramitha, Cindy Putra, Reza Ananda Rahma, Intan Dwi Rahmadi, Diky Ramadhan, Muhammad Yakub Rambe, Lima Hartima Rambe, Lima Hartimar Rofiqoh Dewi Roslina Roslina, Roslina Sagala, Tamado Simon Sari, Rita Novi Sari, Rita Novita Setiawan, Adil Simanullang, Maradona Jonas Siregar, Kiki Putri Ani Situmorang, Zakaria sri lestari rahayu Subhan, Zhafira Nur Sugeng Riyadi Sukriatna Sumantri, Ekoliyono Wahyu Suyono Suyono Syahrian, Achmad Tambunan, Fazli Nugraha Tarigan, Dede Ardian Teddy Gunawan, Teddy Teddy Surya Gunawan Veronica Wijaya, Veronica Wahyudi, Diky Wahyuni, Linda Wanayaumini, W Wanayumini Zai, Andreas Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.