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All Journal Sistemasi: Jurnal Sistem Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Karya untuk Masyarakat (JKuM)
Egi Dio Bagus Sudewo
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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Penyuluhan Karir di Era Industri: Menjadi Praktisi atau Akademisi (Studi Kasus di SMK Muhammadiyah Bangunjiwo) Egi Dio Bagus Sudewo; Virdiana Sriviana Fatmawaty; Mufaddal Al Baqir; Murinto -; Abdul Fadlil
Jurnal Karya untuk Masyarakat (JKuM) Vol 5, No 2 (2024): Jurnal Karya untuk Masyarakat
Publisher : STARKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36914/jkum.v5i2.1183

Abstract

Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk mengevaluasi pengaruh penyuluhan karir terhadap pilihan profesi siswa di SMK Muhammadiyah Bangunjiwo, Kabupaten Bantul, Daerah Istimewa Yogyakarta. Masalah utama yang dihadapi adalah kurangnya pemahaman siswa tentang pilihan karir yang sesuai dengan minat, bakat, dan potensi mereka apakah mereka ingin menjadi seorang praktisi atau akademi. PKM ini bertujuan untuk mengevaluasi dampak penyuluhan karir terhadap pemahaman siswa tentang pilihan profesi. Metode penyuluhan yang digunakan meliputi penyuluhan terstruktur melalui presentasi dan diskusi, sesi tanya jawab, dan penggunaan media digital. Hasil kuisioner awal menunjukkan mayoritas siswa belum memutuskan karir. Setelah penyuluhan, terjadi perubahan signifikan: 13 siswa memilih menjadi praktisi di bidang teknologi informasi, 6 siswa cenderung ingin menjadi praktisi, 5 siswa cenderung memilih karir sebagai akademisi, 4 siswa pasti ingin menjadi akademisi, dan hanya 2 siswa yang masih belum memutuskan. Penyuluhan ini terbukti memperluas wawasan dan meningkatkan kepercayaan diri siswa dalam menentukan masa depan karir mereka. PKM ini menunjukkan bahwa penyuluhan karir yang efektif sangat penting dalam membantu siswa SMK menentukan pilihan karir yang sesuai. ABSTRACT This Community Service Program (PKM) aims to evaluate the impact of career counseling on the career choices of students at SMK Muhammadiyah Bangunjiwo, Bantul Regency, Special Region of Yogyakarta. The main issue faced is the students' lack of understanding regarding career choices that align with their interests, talents, and potentials, whether they want to become practitioners or academicians. The PKM aims to assess the impact of career counseling on students' understanding of career options. The counseling methods used include structured counseling through presentations and discussions, Q&A sessions, and the use of digital media. Initial questionnaire results showed that the majority of students had not decided on a career. After the counseling sessions, there was a significant change: 13 students chose to become practitioners in the field of information technology, 6 students tended to want to become practitioners, 5 students tended to choose a career as academicians, 4 students were certain they wanted to become academicians, and only 2 students remained undecided. This counseling proved to expand the students' horizons and increase their confidence in determining their future careers. This PKM demonstrates that effective career counseling is crucial in helping SMK students determine suitable career choices.
JAVANESE SCRIPT HANACARAKA CHARACTER PREDICTION WITH RESNET-18 ARCHITECTURE Sudewo, Egi Dio Bagus; Biddinika, Muhammad Kunta; Fadlil, Abdul
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 2 (2024): Maret 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.3017

Abstract

Abstract: This study aims to train computers to recognize Javanese script characters known as Hanacaraka. The evaluation was conducted on the use of Convolutional Neural Network (CNN) with the ResNet-18 architecture in recognizing these characters. The research objective is to overcome traditional character recognition barriers and improve accuracy. The method employed includes building a CNN model with the ResNet-18 architecture and using diverse datasets. The results show a training accuracy of 100%, validation accuracy of 98.01%, and accuracy, precision, recall, and F1-score each at 100%. This study concludes that the developed model successfully achieves a high level of accuracy and contributes positively to the development of Javanese Hanacaraka character recognition technology. Keywords: convolution neural network (CNN); javanese hanacaraka script; resnet-18           Abstrak: Penelitian ini bertujuan melatih komputer untuk mengenali huruf aksara Jawa Hanacaraka. Evaluasi dilakukan terhadap penggunaan Convolutional Neural Network (CNN) dengan arsitektur ResNet-18 dalam pengenalan karakter tersebut. Tujuan penelitian adalah mengatasi hambatan pengenalan karakter tradisional dan meningkatkan akurasi. Metode yang digunakan mencakup pembuatan model CNN dengan arsitektur ResNet-18 dan penggunaan dataset yang beragam. Hasilnya menunjukkan akurasi pelatihan 100%, validasi 98.01%, dan akurasi, presisi, recall, dan F1-score masing-masing sebesar 100%. Simpulan penelitian ini adalah bahwa model yang dikembangkan berhasil mencapai tingkat akurasi yang tinggi dan memberikan kontribusi positif pada pengembangan teknologi pengenalan karakter Hanacaraka Jawa.Kata kunci: convolution neural network (CNN); huruf aksara jawa hanacaraka; resnet-18 
Implementasi Metode ARAS Dalam Menentukan Kelayakan Bakal Calon Formatur Sudewo, Egi Dio Bagus; Suryanata, Muhammad Gilang; Ibnutama, Khairi; Al Hafiz, Afdal
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 7 No. 1 (2024): J-SISKO TECH EDISI JANUARI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v7i1.9508

Abstract

Ikatan Pelajar Muhammadiyah adalah salah satu oraganisasi pelajar islam yang merupakan organisasi otonom Muhammadiyah, didirikan pada tanggal 18 juli 1961 oleh Pemuda Muhammadiyah atas dasar untuk menjaga Pelajar Muhammadiyah dari paham radikalisme dan komunis, penentuan bakal calon formatur yang layak untuk dipilih merupakan salah satu masalah yang terjadi karena musyawarah pemilihan harus dilakukan untuk tetap melanjutkan regenerasi kepemimpinan organisasi. Untuk mengatasi masalah tersebut diperlukan adanya Sisem Pendukung Keputusan dalam menentukan kelayakan bakal calon formatur dengan menggunakan metode Additive Ratio Assessment (ARAS). Metode ARAS adalah melakukan perangkingan dengan cara membandingkan dengan alternatif lainnya sehingga mendapatkan hasil yang ideal dan terbaik. Dengan adanya sistem tersebut kinerja dan waktu pengambilan keputusan menentukan kelayakan bakal calon formatur untuk musyawarah lebih efektif dan efisien baik dari segi kecepatan dalam mengambil keputusan.
Analysis of School Life Expectancy Prediction in North Sumatra using the ARIMA Method for the 2024-2025 Period Sudewo, Egi Dio Bagus; Biddinika, Muhammad Kunta; Dahlan, Khoirul Anam; Prayitno, Kintung; Kariyamin, Kariyamin
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4611

Abstract

This study analyzes the projection of the School Life Expectancy (HLS) in 33 districts/cities in North Sumatra Province using the ARIMA method. Historical HLS data from 2019 to 2023 were used to forecast the HLS values for 2024 and 2025. The prediction results show an increase in HLS in most regions, with several districts/cities such as Labuhan Batu, Pematangsiantar City, and Padangsidempuan City experiencing significant growth. However, some regions like Mandailing Natal and Samosir show a more stable trend without significant increases. These findings indicate disparities in access and quality of education across various regions in North Sumatra. Overall, the ARIMA model provides a positive forecast for future HLS improvements, though regional disparities require special attention from relevant authorities to promote equal access to education.
FORECASTING THE JAKARTA COMPOSITE INDEX USING LSTM BASED ON INDONESIAN MARKET DATA Yunita, Reni; Egi Dio Bagus Sudewo; Azyana Alda Sirait
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4310

Abstract

Abstract: The capital market plays an important role in describing the economic conditions of a country, and the IHSG is used as the main indicator to observe the movement of all stocks on the Indonesia Stock Exchange. Because stock data is volatile and non-linear, the forecasting process becomes challenging, requiring methods that can capture historical patterns more accurately. This study aims to predict IHSG movements using the Long Short-Term Memory (LSTM) model to generate stable short-term predictions. Historical IHSG data was used to train the model, and accuracy was evaluated using Mean Squared Error (MSE). The results show that the model obtained an MSE 6784.0207, RMSE 82.3652 and MAPE 0.88%, indicating a relatively low prediction error rate. The visualization shows that the model's predictions are very close to the actual data, and the 60-day forecasting results show a potential increase in the IHSG of 1.05%. Thus, the LSTM model is capable of providing fairly accurate IHSG predictions and can be a useful tool for investors in analyzing short-term market movements. Keywords: forecasting; JCI; long short term memory Abstrak: Pasar modal memiliki peran penting dalam menggambarkan kondisi ekonomi suatu negara, dan IHSG digunakan sebagai indikator utama untuk melihat pergerakan seluruh saham di Bursa Efek Indonesia. Karena data saham bersifat fluktuatif dan tidak linear, proses peramalan menjadi tantangan, sehingga dibutuhkan metode yang mampu menangkap pola historis secara lebih akurat. Penelitian ini bertujuan memprediksi pergerakan IHSG menggunakan model Long Short-Term Memory (LSTM) untuk menghasilkan prediksi jangka pendek yang stabil. Data historis IHSG digunakan untuk melatih model, kemudian akurasi dievaluasi menggunakan Mean Squared Error (MSE). Hasil penelitian menunjukkan bahwa model memperoleh nilai MSE 6784.0207, RMSE 82.3652 dan MAPE 0.88% yang menandakan tingkat kesalahan prediksi relatif rendah. Visualisasi menunjukkan bahwa prediksi model sangat mendekati data aktual, dan hasil forecasting 60 hari ke depan memperlihatkan potensi kenaikan IHSG sebesar 1,05%. Dengan demikian, model LSTM mampu memberikan prediksi IHSG yang cukup akurat dan dapat menjadi alat bantu bagi investor dalam menganalisis pergerakan pasar jangka pendek. Kata kunci: peramalan; JCI; memori jangka pendek
DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character Egi Dio Bagus Sudewo; Muhammad Kunta Biddinika; Abdul Fadlil
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3855

Abstract

This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese
Co-Authors Abdul Fadlil Abdul Fadlil Al Hafiz, Afdal Azyana Alda Sirait Ibnutama, Khairi Kariyamin, Kariyamin Khoirul Anam Dahlan Kintung Prayitno, Kintung Mhd Gilang Suryanata Mufaddal Al Baqir Muhammad Kunta Biddinika Muhammad Kunta Biddinika Murinto - Virdiana Sriviana Fatmawaty Yunita, Reni