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All Journal AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat CSRID SISFOTENIKA
Nurdiansah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Social Sciences

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Implementasi Metode Certainty Factor Pada Sistem Pakar Untuk Mendiagnosis Gangguan Mental Berbasis Android Nurdiansah; Cucut Susanto, Cucut; T, Husain; Irmawati; Bahtiar, Akbar
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 1: February 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.1.2023.22-36

Abstract

Good mental health is a condition when our mind is in a state of calm and calm, allowing us to enjoy everyday life and appreciate others around us. The importance of mental health for life because it can affect how a person thinks. Mental disorders can change the way a person handles stress, relates to others, makes choices, and triggers the desire to hurt oneself. In this study, the author intends to design and build an expert system to diagnose android-based mental disorders using the certainty factor method. The certainty factor method is a method used to measure something whether it is certain or uncertain in diagnosing and identifying a problem. In whitebox testing, conclusions are drawn, from the overall results of testing the applications made are free from logical errors, this can be seen from the results of calculations for the number of Cyclomatic Complexity (CC) is 12, Region = 12 and IndependentPath = 12, all values are the same. In the blackbox testing, conclusions are drawn, the overall results of the input and output testing of the application that are made are in accordance with the desired specifications. a value of 73% means the system can work well.
Penerapan Tools Digital sebagai Media Penguatan Pembelajaran Kolaboratif di Kelompok Kerja Guru (KKG) Malabiri Nurdiansah; Ahyuna; Andrew Ridow Johanis M; Arham Arifin; Arwansyah; Faizal; Hasyrif Sy; Herman Heriadi; Magfirah; Suryani; Arifin, Suci Ramadhani
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 9 No 3 (2025): Agustus
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v9i3.26408

Abstract

Program pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi digital anggota Kelompok Kerja Guru (KKG) Malabiri dalam mengembangkan media penilaian dan pembelajaran interaktif menggunakan Google Form dan Canva. Metode yang digunakan adalah pelatihan praktik langsung dalam workshop satu hari yang diikuti oleh 56 guru sekolah dasar di Kecamatan Minasatene, Kabupaten Pangkajene Kepulauan. Kegiatan dilakukan dalam tiga tahap: persiapan (analisis kebutuhan dan pengembangan modul), pelaksanaan (pelatihan Google Form dan Canva), serta evaluasi (pre-test dan post-test). Hasil program menunjukkan peningkatan pemahaman yang signifikan pada peserta, dengan rata-rata skor meningkat dari 42,1 menjadi 80,9. Secara khusus, pemahaman Google Form meningkat sebesar 36,8% dan Canva sebesar 40,8%. Setiap peserta berhasil mengembangkan setidaknya satu instrumen penilaian digital dan satu media pembelajaran interaktif yang siap digunakan. Program ini membuktikan bahwa pendekatan pelatihan praktik langsung efektif dalam meningkatkan kompetensi digital guru sekolah dasar. Kelompok Kerja Guru (KKG) Malabiri di Kecamatan Minasatene menghadapi tantangan adaptasi pembelajaran digital, dimana 78% guru masih menggunakan metode evaluasi konvensional berbasis kertas, 85% belum pernah menggunakan platform desain digital untuk media pembelajaran, dan 67% membutuhkan bimbingan teknis dalam mengintegrasikan tools digital. Program pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi digital anggota KKG Malabiri dalam mengembangkan media penilaian dan pembelajaran interaktif menggunakan Google Form dan Canva. Metode yang digunakan adalah pelatihan praktik langsung dalam workshop satu hari yang diikuti oleh 56 guru sekolah dasar. Kegiatan dilakukan dalam tiga tahap: persiapan, pelaksanaan, dan evaluasi menggunakan uji t-test berpasangan untuk membandingkan pre-post test. Hasil menunjukkan peningkatan pemahaman yang signifikan (p<0,001), dengan rata-rata skor meningkat dari 42,1 menjadi 80,9. Pemahaman Google Form meningkat 36,8% dan Canva 40,8%. Setiap peserta berhasil mengembangkan instrumen penilaian digital dan media pembelajaran interaktif. Program ini membuktikan pelatihan praktik langsung efektif meningkatkan kompetensi digital guru sekolah dasar.
2Deep Model Prediksi Berbasis Weighting Average Untuk Time Series Data Arwansyah; Cucut Susanto; Nurdiansah
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i2.462

Abstract

In time series data analysis, the need for accurate and efficient predictive models is becoming increasingly urgent as data complexity rises. This study proposes the 2Deep Model, a hybrid approach that combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Stacked LSTM, utilizing the Weighting Average technique to optimize predictions. This method was chosen for its potential in handling long-term dependencies and temporal complexity in data. Experiments were conducted on five datasets: ETTh1, ETTh2, ETTm1, ETTm2, and AQI Shanghai. The results show that the proposed model achieves low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values on the first four datasets, with an average MSE of 0.0289 and an MAE of 0.0971, along with a relatively high R-squared (R²) value. However, for the AQI Shanghai dataset, the model's performance declined, with higher MSE and MAE values and a lower R². These findings indicate that the 2Deep Model holds significant potential for time series data prediction applications, although there is room for improvement when dealing with more diverse datasets. Future research suggestions include further model optimization and exploring other hybrid methods to enhance model generalization.
Co-Authors Ahyuna Andrew Ridow Johanis M Arham Arifin Arifin, Suci Ramadhani Arwansyah Bahtiar, Akbar Cucut Susanto Cucut Susanto, Cucut Faizal Hasyrif SY Herman Heriadi Irmawati Magfirah SURYANI T, Husain