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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika dan Rekayasa Elektronik Jurnal Infortech Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF)
Suryadithia, Rachmat
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Analisis Review Pengguna Google Meet dan Zoom Cloud Meeting Menggunakan Algoritma Naïve Bayes Rezki, Muhammad; Kholifah, Desiana Nur; Faisal, Muhammad; Priyono, Priyono; Suryadithia, Rachmat
Jurnal Infortech Vol 2, No 2 (2020): Desember 2020
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v2i2.9286

Abstract

Saat ini seluruh dunia sedang menghadapi wabah penyakit menular yaitu virus Covid 19. Pembatasan sosial atau menjaga jarak adalah serangkaian tindakan pengendalian infeksi nonfarmasi yang dimaksudkan untuk menghentikan atau memperlambat penyebaran penyakit menular tersebut. Sehingga seluruh masyarakat diharapkan untuk beraktifitas dirumah untuk menghentikan penyebaran virus Covid 19. Agar tetap bisa menjalankan aktifitas dirumah diperlukan virtual meet untuk berkomunikasi sesama team atau rekan kerja. Saat ini virtual meet telah banyak dipakai. Penilaian Sebuah Aplikasi di Playstore memiliki tujuan untuk memberikan ulasan tentang kelebihan dan kekurangan dalam penggunaan aplikasi khsusunya virtual video conference. Untuk mengetahui sejauh mana analisa review pengguna aplikasi Google Meet dan Zoom Cloud Meeting berdasarkan pemberian jumlah bintang dengan menggunakan teknik klasifikasi yaitu perbandingan Algoritma Naïve Bayes dengan feature optimasi SMOTE Upsampling dan PSO. Penggunaan feature selection synthetic minority over-sampling technique (SMOTE) dan feature optimasi Particle swarm optimization (PSO) pada algoritma klasifikasi terbukti sangat berpengaruh untuk meningkatkan akurasi pada algoritma Naïve Bayes untuk pengolahan data review pengguna Google Meet dan Zoom Cloud Meeting pada google play berdasarkan perolehan skor bintang. Hasil pengujian mendapatkan hasil akurasi sebesar 85,76 % yang ditambah dengan Feature Smote dan PSO untuk review Zoom Cloud Meeting, sedangkan untuk pengguna Google Meet yang ditambah dengan Feature Smote dan PSO­ hanya mampu mendapat tingkat akurasi sebesar 79,33 %.
Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks Pangesti, Witriana Endah; Ariyati, Indah; Priyono; Sugiono; Suryadithia, Rachmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13161

Abstract

The prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic algorithms to improve the accuracy of stu-dent graduation predictions, measure the improvements obtained, and compare the accuracy results between the genetic algorithm-optimized neural network model and the neural network model without optimization. The training process of the neural network model is conducted using training data obtained through experiments, and the accuracy results of the neural network model with and without genetic algorithm optimization are compared. The research findings indicate that by harnessing genetic algorithms to optimize the parameters of the neural network model, the accuracy of student graduation predictions increased by 2.78%. Furthermore, the Area Under the Curve (AUC) also improved by 0.037%. These results demonstrate that integrating genetic algorithms into the neural network model can significantly enhance prediction performance. Thus, this study successfully utilized genetic algorithms to improve student graduation predictions using a neural network model. Experimental results show that prediction accuracy and AUC values significantly increased after opti-mizing the neural network model's parameters with genetic algorithms. Therefore, the use of genetic algorithms can be considered an effective approach to improving student graduation predictions, thereby assisting educa-tional institutions in improving efficiency and helping students graduate on tim.
Implementasi Smart Governance Melalui Layanan Digital Berbasis Web di Desa Jamali Kabupaten Cianjur Jawa Barat Darmadi, Roby; Nugraha, Mara; Fadlilah, Fikri; Suryadithia, Rachmat; Al Kautsar, Hanggoro Aji
Jurnal Pengabdian UNDIKMA Vol. 6 No. 1 (2025): February
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v6i1.14558

Abstract

This community service aims to implement the concept of smart villages through the application of web-based information systems to support smart governance of village governments that are more efficient and transparent. The method used in this community service activity is training and mentoring with the stages of planning, design, development, expert judgment, and system implementation which includes digital information services, making certificates, community complaints, and managing population data. The evaluation instrument for this activity uses a questionnaire with quantitative descriptive data analysis techniques.  The results of this service activity show that the web-based information system developed has succeeded in increasing efficiency in the village administration process, accelerating access to information for the community, and increasing transparency in public services. The training provided was able to improve the understanding and ability of village officials in using the system. The implementation of a digital information system in Jamali Village is expected to encourage the creation of a more responsive and efficient village government, providing a positive impact on improving the quality of public services in the future.
DECISION TREE OPTIMIZATION IN HEART FAILURE DIAGNOSTICS: A PARTICLE SWARM OPTIMIZATION APPROACH Sumarna, Sumarna; Sartini, Sartini; Pangesti, Witriana Endah; Suryadithia, Rachmat; Riyanto, Verry
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1815

Abstract

The rapid advancement of technology has made the implementation of accurate diagnostic methods for serious diseases like heart failure extremely important. Heart failure, being a leading cause of death worldwide, necessitates precise and accurate diagnostic techniques. The problem with conventional diagnostic methods is that they often fail to effectively accommodate the complexity of clinical data, leading to an increase in mortality rates due to heart failure. Previous research has employed various data analysis methods, but there are still fluctuations in the accuracy of results. The aim of this study is to enhance the accuracy of heart failure diagnosis by integrating the Decision Tree (DT) method with Particle Swarm Optimization (PSO) optimization. This research involves collecting and preprocessing heart failure data, followed by the development of a DT model. This model is then optimized using the PSO technique. The study uses a dataset from the UCI Repository, involving testing and validation processes to measure the model's effectiveness. The results show a significant improvement in accuracy and the Area Under Curve (AUC) after applying PSO. Accuracy increased from 79.92% to 85.29%, and AUC from 0.706% to 0.794%. The conclusion is that the integration of DT and PSO successfully improved the accuracy and reliability of the model in diagnosing heart failure. This innovation offers potential for further research in integrating optimization techniques in health data analysis, with the possibility of application in various clinical scenarios.
OPTIMALISASI POHON KEPUTUSAN ID3 MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PREDIKSI ADOPSI LAYANAN DIGITAL PAYMENT Sumarna; Wijaya, Ganda; Suryadithia, Rachmat; Pangesti, Witriana Endah; Yudhistira
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 2 (2025): JIRE November 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i2.1711

Abstract

Transformasi digital mendorong peningkatan penggunaan layanan pembayaran digital seperti OVO dan GoPay. Namun, tingkat penggunaan layanan ini belum merata, sehingga diperlukan model prediksi untuk memahami faktor-faktor yang memengaruhi keputusan pengguna dalam mengadopsi layanan tersebut. Penelitian ini mengembangkan model klasifikasi berbasis algoritma ID3 yang dioptimasi menggunakan Particle Swarm Optimization (PSO). Data dikumpulkan melalui kuesioner dari 750 responden, kemudian diproses melalui tahap preprocessing, pelatihan ID3, dan optimasi dengan PSO. Hasil menunjukkan bahwa model ID3+PSO mencapai akurasi 94,53%, lebih tinggi dibandingkan ID3 tanpa optimasi (92,93%). Precision dan recall masing-masing meningkat menjadi 95,41% dan 95,15%, sementara AUC tetap tinggi di angka 98,20%. PSO terbukti efektif menyederhanakan model dan meningkatkan performa klasifikasi. Temuan ini berimplikasi pada peningkatan akurasi sistem rekomendasi dan pengambilan keputusan strategis oleh penyedia layanan digital payment, terutama dalam memahami karakteristik serta potensi adopsi layanan oleh pengguna secara lebih tepat.
Co-Authors Fadlilah, Fikri Ganda Wijaya, Ganda Hanggoro Aji Al Kautsar Indah Ariyati Kholifah, Desiana Nur Muhammad Faisal Nugraha, Mara Priyono Priyono Priyono Rezki, Muhammad Sartini Sartini Sugiono, Sugiono Sumarna Sumarna Sumarna Verry Riyanto WITRIANA ENDAH PANGESTI Yudhistira