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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Kimia Undiksha Jurnal Penelitian Pendidikan IPA (JPPIPA) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Sukemi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Materials Science & Nanotechnology Physics Social Sciences

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Klasifikasi Data Penderita Skizofrenia Menggunakan CNN-LSTM dan Cnn-Gru pada Data Sinyal EEG 2D Firmansyah; Rini, Dian Palupi; Sukemi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1072

Abstract

Schizophrenia (SZ) is a brain disease with a chronic condition that affects the ability to think. Common symptoms that are often seen in SZ patients are hallucinations, delusions, abnormal behavior, speech disorders, and mood disorders. SZ patients can be diagnosed using electroencephalographic (EEG) signals. This study conducted a comparative analysis of the best method in EEG classification using the Deep Learning (DL) method. The author uses the 2D Convolutional Neural Network (2D-CNN) method with different layers. The first 2D-CNN uses a layer of Long Short Term memory(LSTM) and Gate Recurrent Unit(GRU). The dataset used consists of two types of EEG signals obtained from 39 healthy individuals and 45 schizophrenic patients during a resting state. Test results for the accuracy of the F1-score from 5 times testing the CNN method using the LSTM layer has the best accuracy value of 94.12% and 5 times testing the CNN method using the GRU layer has the best accuracy value of 94.12%.
Pengenalan Kepribadian Melalui Tulisan Tangan Menggunakan Convulutional Neural Network Dengan LS Classifiers Guntara, Yusa Virginiawan; Syamsuryadi; Sukemi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.15193

Abstract

A person's handwriting is different and unique, even though it looks similar it is certainly not the same as someone else's writing. One's personality traits can be identified based on handwriting. One of the implementations is (handwriting recognition). To identify a person's personality, it can be classified by handwriting using the 'Graphology' field. The computational system to identify handwritten images can use the Convulution Neural Network method. Using the CNN method is expected to produce good accuracy with a low error rate. The CNN method is able to predict a person's personality through manuscripts as images. In addition, to increase the diversity of classifications, the Least Squared Classifiers method is needed. . LS Classifiers are designed to increase the variety of CNN methods in feature extraction and classification. The LS Classifier method is a classification method that estimates the w parameter vector and takes the best linear classifier based on the w parameter vector. Research has functions for users, including to find out someone's personality, especially extrovert and introvert personality. In this study CNN serves as Feature Extraction to classify Image and Ls Classifiers serves to increase diversity into 2 personality groups. The level of accuracy of the performance of the CNN & Ls Classifiers method in carrying out feature extraction and classification of handwritten images in determining personality has a good level of accuracy.
PENINGKATAN KEMAMPUAN SISWA SMKN 1 TANJUNG PANDAN BELITUNG DALAM SIMULASI ONLINE SISTEM PALANG PINTU KERETA API Fali Oklilas, Ahmad; Abdurahman; Rossi Passarella; Sukemi; Muhammad Ali Buchari
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 1 No. 5 (2023): Oktober
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v1i5.136

Abstract

Sekolah Menengah Kejuruan atau SMK sangat membutuhkan praktik lebih banyak dibandingkan teori. Untuk praktikum diperlukan sarana dan prasarana yang menunjang, namun tidak semua sarana dapat terpenuhi mengingat keterbatasan dana yang disediakan pihak sekolah dan pemerintah daerah. Permasalahan inilah perlu diatasi agar siswa SMK dapat mengikuti perkembangan zaman dan teknologi terakhir. Untuk memecahkan masalah ini dapat dengan cara menggunakan simulasi online sebagai pengganti peralatan praktikum. Dosen Jususan Sistem Komputer Fakultas Ilmu Komputer dalam rangka pengabdian masyarakat memecahkan masalah di sekolah SMK Negeri 1 Tajung Pandan Belitung dengan cara memberi pelatihan kepada siswa disana berupa pemanfaatan simulasi online sistem palang pintu otomatis untuk studi kasus pintu kereta api. Metode pengajaran yang digunakan full praktik menggunakan komputer karena dilaksankan secara luring di laboratorium sekolah yang bersangkutan. Hasil yang didapat pemahaman siswa dapat menyelesaikan studi kasus dari simulasi yang ditugaskan serta banyak mendapat sambutan yang antusian dari siswa. Pelatihan ini sangat membawa manfaat dan pengetahuan baru karena keterbatasan sarana dan prasarana yang ada di sekolah dapat diatasi dengan pemanfaat praktikum menggunakan simulasi online.
Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.
Kajian Literatur Penggunaan Instagram sebagai Media Pembelajaran dalam Pembelajaran Kimia Iradha Umi Hajar, Ayu; Novira Rahmadhani, Ade; Husna Herlianti, Zyahdatul; Saputera, Adam; Sukemi
Jurnal Pendidikan Kimia Undiksha Vol. 9 No. 1 (2025)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpk.v9i1.88502

Abstract

Instagram adalah media sosial yang diminati oleh banyak orang sebagai media pembelajaran. Instagram mampu mendukung para guru memberikan materi pembelajaran dengan cara yang menarik dan meningkatkan kreativitas. Tujuan dari studi literatur ini untuk mengetahui kepantasan instagram sebagai media pembelajaran dalam menciptakan minat siswa dan meningkatkan hasil belajar siswa. Penelitian ini dilakukan melalui sistematis narrative literature review dengan pengumpulan artikel dari jurnal nasional dan internasional antara tahun 2017 hingga tahun 2024 dengan database dari Google Scholar. Penggunaan instagram melalui pembelajaran berbasis proyek (PjBL), discovery learning, dan inkuiri terbukti efektif dalam mengoptimalkan pembelajaran kimia. Instagram juga dapat digunakan untuk membuat konten pembelajaran yang menarik, seperti pembuatan infografis poster yang membantu siswa belajar berpikir kritis, serta penerapan kelas eksperimen mampu meningkatkan keinginan siswa dalam belajar. Hasil studi literatur menunjukkan bahwa instagram memiliki potensi besar untuk dijadikan media pembelajaran kimia yang efektif menaikkan hasil belajar dan minat belajar siswa.   Kata Kunci : Instagram, Media Pembelajaran, Pembelajaran Kimia
Bahasa Indonesia Bahasa Indonesia Herliani; Subagiyo, Lambang; Sukemi
Jurnal Penelitian Pendidikan IPA Vol 9 No 8 (2023): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i8.4735

Abstract

Anchored by the interview analysis, the present study explored students’ perceptions of blended learning enactment in Indonesian science classrooms. Data were garnered through questionnaires and semi-structured interviews focusing on students' perceptions of the benefits and challenges of adopting Blended Learning in science classes. Findings suggest that the benefits geared by blended learning in science classes include training students’ learning independence, increasing students’ understanding of science and technology, increasing students’ learning effectiveness, and providing students with a variety of learning experiences. Meanwhile, the challenges depicted in blended learning include practicum activities, internet problems, and economic condition. This study pictured blended learning as an alternative learning method for teachers and students.
Co-Authors Abdurahman Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Buchari, M. Ali Cahyadi, Gabriel Ekoputra Hartono Dian Palupi Rini Dudifa, Aldi Firdaus Firmansyah Guntara, Yusa Virginiawan Herliani Husna Herlianti, Zyahdatul Iradha Umi Hajar, Ayu Kemahyanto Exaudi Lambang Subagiyo Muhammad Ali Buchari Muhammad Naufal Rachmatullah Novira Rahmadhani, Ade Rahmat Fadli Isnanto Ricy Firnando Riyuda, Rafki Sahasika Rossi Passarella Saputera, Adam Sarmayanta Sembiring Siti Nurmaini Syamsuryadi Vindriani, Marsella