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All Journal Proceeding Seminar LPPM UMP Tahun 2014 Pena : Jurnal Ilmu Pengetahuan dan Teknologi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Septian Ari Wibowo
2Program Studi Teknik Informatika Fakultas Teknik, Universitas Muhammadiyah Purwokerto. Jl. Raya Dukuhwaluh. PO BOX 202 Purwokerto, 53182
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Other

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Sistem Pendukung Keputusan Dosen Berprestasi Berdasarkan Kinerja Penelitian dan Pengabdian Masyarakat Hamka, Muhammad; Wibowo, Septian Ari
Proceeding Seminar LPPM UMP Tahun 2014 2014: Proceeding Seminar Hasil Penelitian LPPM 2014, 6 September 2014
Publisher : Proceeding Seminar LPPM UMP Tahun 2014

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Abstract

Pemerintah melalui Kementerian Pendidikan dan Kebudayaan Direktorat Jenderal Pendidikan Tinggi sejak tahun 2004 menyelenggarakan pemilihan dosen berprestasi. Salah satu manfaat pemberian penghargaan ini adalah  meningkatkan  motivasi  di  kalangan  sivitas akademika  untuk  lebih produktif  dalam melaksanakan  tridarma  perguruan  tinggi  dan  mendorong terciptanya iklim akademik di perguruan tinggi. (Dirjendikti, 2013). Penyelenggaraan pemilihan dosen berprestasi tingkat nasional selayaknya didukung melalui pengembangan manajamen akademik di masing-masing perguruan tinggi. Pemilihan dosen berprestasi di Universitas Muhammadiyah Purwokerto (UMP) sebagai seleksi awal dosen berprestasi tingkat nasional belum menjadi program rutin tahunan. Proses pemilihan dosen berprestasi tingkat perguruan tinggi merupakan permasalahan yang melibatkan banyak kriteria yang dinilai (multikriteria), sehingga dalam penyelesaiannya diperlukan sebuah Sistem Pendukung Keputusan (SPK). SPK diharapkan  dapat membantu pengambil keputusan dalam memberikan rekomendasi keputusan dosen berprestasi yang tepat dan lebih obyektif. Artikel ini membahas implementasi metode TOPSIS dapat menyelesaikan permasalahan pengambilan keputusan pada kondisi yang tidak terstruktur dan bersifat multikriteria. Metode TOPSIS mencari solusi ideal pada keputusan yang dihasilkan. Artinya setiap alternatif dinilai  tidak hanya pada kelebihan, akan tetapi juga dinilai dari kelemahannya. Kata Kunci : Dosen Berprestasi; Sistem Pendukung Keputusan; TOPSIS
Evaluasi Model Deep Learning pada Pola Dataset Biomedis Gunawan, Gunawan; Wibowo, Septian Ari; Andriani, Wresti
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 2 (2024): September 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i2.738

Abstract

This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.
Analisis Perbandingan Model Jaringan Saraf Tiruan dan Support Vector Machine dalam Memprediksi Indeks Harga Saham Gabungan Gunawan, Gunawan; Andriani, Wresti; Wibowo, Septian Ari
Pena: Jurnal Ilmu Pengetahuan dan Teknologi Vol. 38 No. 2 (2024): PENA SEPTEMBER 2024
Publisher : LPPM Universitas Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31941/jurnalpena.v38i2.4942

Abstract

The Jakarta Composite Index (IHSG) is a key indicator that reflects the performance of the stock market in Indonesia. It is often used by investors, analysts, and decision-makers to assess economic conditions and make investment decisions. However, the fluctuating and dynamic nature of the stock market makes predicting the IHSG a significant challenge. This study compares the effectiveness of Neural Network (NN) and Support Vector Machine (SVM) with optimization methods such as Particle Swarm Optimization (PSO) and Evolutionary Algorithm (EVO) in predicting stock prices. The results show that the combination of SVM with EVO provides the best prediction accuracy with the lowest error values (RMSE: 0.07, MAE: 0.09, MSE: 0.004). In contrast, NN with PSO and EVO showed higher prediction errors, indicating lower accuracy levels. These findings highlight the potential of optimization methods in enhancing the performance of stock prediction models, with SVM+EVO being the most effective combination.
Co-Authors Andriani, Wresti Gunawan Gunawan Gunawan Muhammad Hamka