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All Journal Jurnal Informatika dan Teknik Elektro Terapan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Building of Informatics, Technology and Science Jurnal Teknik Informatika C.I.T. Medicom MEANS (Media Informasi Analisa dan Sistem) Jurnal Informatika Terpadu JUSTIN (Jurnal Sistem dan Teknologi Informasi) D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Artificial Intelligence and Engineering Applications (JAIEA) J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Manajemen
Dikananda, Arif Rinaldi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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IMPLEMENTASI TEKNOLOGI QUICK RESPONSE CODE DALAM SISTEM E-TICKETING PADA EVENT ORGANIZER Almadina, Muhammad Fitrian Shousyade; Martanto, Martanto; Dikananda, Arif Rinaldi; Rohman, Dede
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5730

Abstract

Penelitian bertujuan untuk merancang dan menguji seberapa efektif sistem dengan teknologi Quick Response Code untuk mengoptimalkan manajemen acara dan mengevaluasi kepuasan pengguna. Tingkat kepuasan diukur menggunakan metode System Usability Scale (SUS) yang dibagikan kepada 60 responden. Analisis kuesioner menghitung rerata nilai final_score SUS, disertai uji validitas dan reliabilitas menggunakan Cronbach's Alpha. Pengujian Kruskal-Wallis dilakukan untuk menilai perbedaan kepuasan sebelum dan setelah sistem diimplementasi. Hasil analisis menunjukkan nilai rerata final_score SUS sebesar 72.2 (kategori GOOD), dengan tingkat kepuasan HIGH hingga ACCEPTABLE. Uji validitas menyatakan semua pertanyaan valid, dan uji reliabilitas menghasilkan nilai Cronbach Alpha sebesar 0.69, hal ini menunjukkan konsistensi yang baik. Uji Kruskal-Wallis mengungkap perbedaan signifikan (p < 0.001), menunjukkan dampak positif sistem terhadap pengalaman pengguna.
Comparing optimization hyperparameter long short term memory for rainfall prediction model Nur Hermawan, Ilham; Martanto, Martanto; Dikananda, Arif Rinaldi; Mulyawan, Mulyawan
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2025.942.pp405-414

Abstract

Improving the accuracy of weather prediction, especially rainfall, is very important in various sectors such as agriculture, water resource management, and disaster mitigation. This research aims to optimize the Long Short-Term Memory (LSTM) model in rainfall prediction through the application of hyperparameter optimization using two main techniques: Grid Search and Bayesian Optimization (Optuna). This hyperparameter optimization includes finding the best configuration of important parameters, such as the number of LSTM units, batch size, learning rate, and number of epochs. A historical rainfall dataset from BMKG is used, which is then divided into training and test data to build and test the prediction model. Grid Search performs a thorough exploration of all possible parameter combinations, while Optuna uses a probabilistic Bayesian approach to speed up the optimization process. The results show that hyperparameter optimization significantly improves the performance of LSTM models. The model optimized with Optuna produces a Mean Squared Error (MSE) value of 0.179578 with an execution time of 105.26 seconds, while Grid Search has an MSE of 0.286778 with an execution time of 457.69 seconds. The lower MSE value indicates that the Optuna model has a smaller prediction error, making it more accurate in predicting rainfall. The faster execution time of Optuna also confirms its efficiency in finding the optimal hyperparameter configuration compared to Grid Search. The conclusion of this study confirms that hyperparameter optimization plays an important role in improving the prediction accuracy of LSTM for rainfall. The developed method is expected to be the basis for the development of other weather prediction models as well as support decision-making in various sectors that rely on weather prediction. In addition, this research opens up opportunities for further studies in the optimization of deep learning models in handling complex climate data.
PENERAPAN FP-GROWTH UNTUK ANALISIS POLA PEMBELIAN PRODUK SKINCARE Khoirunisa, Pitria; Martanto, Martanto; Dikananda, Arif Rinaldi; Rohman, Dede
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5213

Abstract

Kemajuan teknologi informasi dan persaingan di industri kecantikan menghasilkan data transaksi yang besar. Algoritma FP-Growth dipilih dalam penelitian ini karena efisiensinya dalam menganalisis data besar tanpa perlu menghasilkan kandidat itemset seperti algoritma Apriori. Data ini, jika dianalisis dengan tepat, dapat memberikan wawasan yang berguna untuk memperbaiki strategi pemasaran, meningkatkan efisiensi operasional, dan meningkatkan kepuasan pelanggan. CV Leika Skincare belum memiliki panduan dalam memanfaatkan data transaksi secara optimal. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian produk skincare menggunakan data transaksi penjualan dari Januari hingga Juni 2024, terdiri dari 25.818 entri data dan 22 atribut. Data dalam industri kecantikan sangat penting karena memberikan wawasan terkait perilaku pelanggan, preferensi produk, dan kebutuhan pasar, sehingga membantu perusahaan seperti CV Leika Skincare dalam merancang kampanye pemasaran yang relevan dan berbasis data. Dengan metode FP-Growth, bagian dari pendekatan Knowledge Discovery in Databases (KDD), serta bantuan perangkat lunak Rapid Miner, ditemukan 10 aturan asosiasi yang signifikan. Hasil penelitian ini memberikan wawasan strategis untuk promosi bundling produk, rekomendasi produk, dan manajemen stok yang lebih baik. Penelitian ini menunjukkan pentingnya pemanfaatan data secara strategis dalam meningkatkan daya saing di industri kecantikan.
MENINGKATKAN EFISIENSI PANEL SURYA MELALUI IOT BERBASIS ARDUINO Hamam, Moh; Martanto, Martanto; Dikananda, Arif Rinaldi; Rifa'i, Ahmad
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5231

Abstract

Krisis energi global dan meningkatnya kebutuhan akan solusi ramah lingkungan menjadikan energi surya sebagai salah satu alternatif utama. Namun, efisiensi panel surya tradisional sering terhambat oleh posisi yang statis, menyebabkan penyerapan energi kurang optimal. Penelitian ini bertujuan untuk meningkatkan efisiensi panel surya dengan sistem otomatis berbasis Internet of Things (IoT) dan Arduino. Metode yang digunakan mencakup pengembangan prototipe yang mengintegrasikan sensor Light Dependent Resistor (LDR) untuk mendeteksi intensitas cahaya, mikrokontroler Arduino untuk pengendalian, dan aplikasi Blynk untuk pemantauan real-time. Data intensitas cahaya digunakan untuk mengatur posisi panel surya secara otomatis mengikuti perubahan posisi matahari sepanjang hari. Hasil penelitian menunjukkan bahwa sistem ini mampu meningkatkan efisiensi penyerapan energi panel surya secara signifikan. Sistem ini memungkinkan pemantauan dan pengendalian yang mudah, menjadikannya solusi yang ekonomis dan praktis untuk energi terbarukan yang lebih efisien
Media Pembelajaran Media Pembelajaran Fiqih dan Tajwid Berbasis Game Edukasi “Ngazee : Finding Hidayah” Menggunakan Metode MDLC Bahrul Jawahir, Muhammad; Dana, Raditya Danar; Dikananda, Arif Rinaldi
D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Vol 14 No 1 (2024): Edisi Januari 2024
Publisher : Universitas Cokroaminoto Palopo Fakultas Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/dcomputare.v14i1.71

Abstract

Perkembangan game saat ini berkembang dengan sangat cepat, dari permainan atau game konvensional dan tradisional bertransformasi menjadi game yang canggih dan modern dengan memanfaatkan kemajuan teknologi. Saat ini game dapat diakses dan dimainkan kapan saja dan dimana saja di berbagai platform. Zaman sekarang ini game tidak hanya dimainkan sebagai hiburan, namun sudah banyak game edukasi yang digunakan sebagai media pembelajaran dan pelayanan di berbagai lembaga pendidikan maupun lembaga masyarakat. Salah satu jenis game yang populer adalah game open world atau game simulasi. Media pembelajaran kitab kuning di Pondok Pesantren hanya bisa didapat atau diperoleh ketika sedang mondok di pesantren dengan tatap muka, ketika siswa atau santri sudah keluar atau lulus dari pesantren mereka tidak dapat mengulang pembelajaran dengan informasi yg sama. Dengan adanya game edukasi pembelajaran kitab kuning maka memungkinkan pembelajaran dapat diakses secara berulang.
Comparison of Sentiment Analysis Models Enhanced by Naïve Bayes and Support Vector Machine Algorithms on Mobile Banking BRImo Reviews Ramadan, Muhamad Firly; Martanto; Dikananda, Arif Rinaldi; Rifa'i, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.732

Abstract

This study compares the effectiveness of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment regarding the BRImo application. User reviews were obtained from the Google Play Store platform and underwent a text preprocessing stage to clean and prepare the data. Subsequently, the SVM and Naïve Bayes algorithms were applied for sentiment analysis, using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that SVM achieved a training accuracy of 95.67% and a testing accuracy of 83.11%, with its best performance on positive sentiment (precision 92.26%, recall 91.79%, F1-score 92.02%) and moderate performance on negative sentiment (precision 62.81%, recall 62.81%, F1-score 62.81%). Meanwhile, Naïve Bayes recorded a training accuracy of 95.23% and a testing accuracy of 82.77%, with its highest performance on positive sentiment (precision 90.12%, recall 93.38%, F1-score 91.72%) but lower performance on negative sentiment (precision 65.07%, recall 60.06%, F1-score 62.46%). In terms of sentiment distribution, SVM was more effective in handling sentiment variations, particularly in detecting negative and neutral sentiments. These findings indicate that SVM outperforms Naïve Bayes in sentiment analysis of user reviews for the BRImo application.
Support Vector Regression to Improve Ethereum Price Prediction for Trading Strategies Muhamad Abdul Fatah; Martanto; Dikananda, Arif Rinaldi; Rifai, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.740

Abstract

Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students Artoti, Azzahra Rizky; Martanto; Dikananda, Arif Rinaldi; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.777

Abstract

The development of technology in education opens up opportunities for innovation to create interactive learning media, especially for early childhood. This research aims to develop educational games based on Smart Apps Creator using the ADDIE method to introduce animal species to Al-Washliyah kindergarten students. The method used is ADDIE, consisting of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. in this study conducted validity, reliability, normality, homogeneity, and anova tests to measure the effectiveness of this learning media. The results showed that this animal species recognition educational game succeeded in improving student understanding with an average score before the use of learning media of 59.2% increasing to 87.73% after using learning media. Validity and reliability tests show that this learning media meets the criteria of effective, easy-to-use, and interesting learning media.
Pengembangan Model Pengelompokan Jenis Bencana Alam di Jawa Barat menggunakan Algoritma K-Means Pura, Panji Adi; ., Martanto; Dikananda, Arif Rinaldi; Rohman, Dede
Bianglala Informatika Vol 13, No 1 (2025): Bianglala Informatika 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v13i1.24700

Abstract

Salah satu masalah terbesar yang dihadapi masyarakat Jawa Barat adalah bencana alam. Analisis berbasis data diperlukan untuk memahami pola kejadian bencana dan mendukung kebijakan mitigasi yang efektif karena berbagai jenis bencana. Untuk menganalisis data kejadian bencana di Jawa Barat selama periode 2020–2023, penelitian ini menggunakan pendekatan Knowledge Discovery in Databases (KDD).Tahapan KDD meliputi pembuatan dataset, preprocessing untuk normalisasi dan penanganan data hilang, serta transformasi guna menentukan atribut utama. Algoritma K-Means digunakan dalam proses data mining untuk mengelompokkan wilayah berdasarkan jenis bencana dan intensitasnya. Tahap terakhir adalah interpretasi hasil, yang bertujuan untuk memahami pola distribusi bencana. Hasil klasterisasi menghasilkan lima kluster utama. Cluster 0 menunjukkan dominasi kejadian banjir dan kebakaran lahan, sering ditemukan di dataran rendah dengan karakteristik lingkungan yang rawan pembakaran liar. Cluster 1 didominasi oleh kejadian tanah longsor di wilayah perbukitan yang curah hujannya tinggi. Cluster 2 mencerminkan kombinasi kejadian hujan angin dan kekeringan di daerah pedesaan dengan sumber daya air terbatas. Cluster 3 menunjukkan kejadian bencana dengan frekuensi rendah dan distribusi yang merata, seringkali terkait dengan daerah urban. Sementara itu, Cluster 4 memiliki tingkat heterogenitas tertinggi, mencakup berbagai jenis bencana dengan intensitas bervariasi di wilayah pegunungan dan lembah. Kualitas klasterisasi diukur menggunakan Davies-Bouldin Index (DBI) sebesar 0.085, mengindikasikan pemisahan kluster yang baik. Selain itu, analisis Performance Vector menunjukkan jarak total antar-kluster sebesar 2.311, dengan jarak terbesar pada Cluster 4 (4.672). Selain memberikan wawasan mendalam tentang pola bencana di Jawa Barat, penelitian ini diharapkan dapat membantu dalam perencanaan dan alokasi sumber daya yang lebih tepat sasaran untuk mitigasi bencana.
APPLICATION OF K-MEANS ALGORITHM IN KINDERGARTEN SCHOOL LOCATION CLUSTERING OF SCHOOL SELECTION STRATEGY BY PARENTS Syifa, Nurkhasanah Fadhila; Martanto, Martanto; Dikananda, Arif Rinaldi; Rohman, Dede
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 1 (2025): March 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i1.20202

Abstract

This research aims to improve the kindergarten school location clustering model to support parents' school selection strategies. The main issue raised is the need to understand parents' preferences more deeply in choosing the right school for their children. To achieve this goal, the K-Means algorithm was applied and analyzed to cluster parents' data based on characteristics such as occupation, education, and residential location. This research utilizes a quantitative method with an exploratory descriptive approach. The results showed that the K-Means algorithm successfully formed two clusters with different characteristics. Cluster_0 includes groups with more centralized or close locations, education levels that tend to be low, and types of jobs that are at the lower middle economic level, while cluster_1 groups with more dispersed or distant locations, higher education levels, and jobs that are at higher economic levels. The quality of the resulting clusterization is considered quite good, with a Davies-Bouldin Index (DBI) value of 0.151. The application of the K-Means algorithm is proven to be effective in identifying groups of parents with different preferences, so it can be a foundation for schools in developing more targeted and tailored service strategies. This research makes an important contribution to the application of clustering techniques to support marketing strategies and decision-making in the early childhood education sector.
Co-Authors -, Martanto - ., Martanto abdullah, nur syarief Ade Rizki Rinaldi Ahmad Faqih Ahmad Rifai Almadina, Muhammad Fitrian Shousyade Ardhanur, Ichlas Artoti, Azzahra Rizky Bahrul Jawahir, Muhammad Fasa, Saefullah fatimah, lilis Fithriyani, Nurul Muna Gifthera Dwilestari Hamam, Moh Hardika Hardika, Hardika hermansyah, Muhammad ikhwan adholf Irfan Ali, Irfan Khoirunisa, Pitria khusnul khotimah Martanto Martanto . Mirna Mirna Muhamad Abdul Fatah Muhammad Hilman Mulyawan Mulyawan, Mulyawan Muzani, Muhamad Nur Hermawan, Ilham Nuraeni Nurcholis, Rifki Odi Nurdiawan Pratama, Fidya Arie Pura, Panji Adi Purnamasari, Ade Irma Putri, Haidah Putri, Niken Zeliana Raditya Danar Dana Ramadan, Muhamad Firly Rifa'i, Ahmad Rifa’I, Ahmad Rohman, Dede Rohmat, Cep Lukman Saeful Anwar, Saeful Subandi, Husein Syahri, Ibnu Nava Syifa, Nurkhasanah Fadhila Wijaya, Nurjana Adi Yunus, Shofian