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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Bulletin of Computer Science Research Bulletin of Information Technology (BIT)
Siska Kurnia Gusti
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ANALISA DAN PENERAPAN METODE AHP DAN PROMETHEE UNTUK MENENTUKAN GURU BERPRESTASI Siska Kurnia Gusti
Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Vol 4, No 1 (2018): Februari
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/rmsi.v4i1.4955

Abstract

Dalam penentuan guru berprestasi di Dinas Pendidikan Kota Pekanbaru rutin dilakukan setiap tahunnya. Proses pemilihannya dilakukan dengan cara memilih alternatif guru yang memenuhi syarat berdasarkan kriteria yang sudah ditentukan. Banyaknya kriteria yang harus dinilai pada setiap calon guru berprestasi membuat tim penyeleksi kesulitan, terlebih lagi tidak sedikit masalah muncul akibat penilaian yang sering berubah-ubah. Pada penelitian ini digunakan penggabungan metode Analitycal Hierarchy Process (AHP) dan Preference Ranking Organization Method for Enrichment Evaluation (Promethee), karena kedua metode ini mampu menyelesaikan masalah dengan multikriteria. Kriteria yang digunakan adalah data penilaian dari Pengawas dan Kepala Sekolah, data prestasi akademik, data kualifikasi akademik, data tes psikotes, data pengalaman mengajar, data karya pengembangan profesi, data perencanaan dan pelaksanaan pembelajaran. Metode AHP digunakan untuk menentukan bobot prioritas sedangkan metode Promethee untuk perangkingannya. Tipe preferensi yang digunakan dalam pembuatan sistem ini adalah Kriteria Quasi (Quasi Criterion) dan Kriteria Preferensi Linier (Criterion with linier Preference). Hasil dari penelitian ini dalam bentuk perankingan berdasarkan nilai tertinggi  dari proses penilaian pada penggabungan kedua metode tersebut, sehingga penggabungan kedua metode tersebut layak digunakan dalam pemilihan guru berprestasi.
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Implementasi Model Long Short Term Memory (LSTM) dalam Prediksi Harga Saham Kurniansyah, Juliandi; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2005

Abstract

Stock market investment is gaining popularity, although predicting stock price fluctuations remains challenging. Accurate stock prediction models can assist investors in decision-making. In this research, a Long Short-Term Memory (LSTM) model was employed to make predictions regarding the stock prices of BBCA based on daily historical data from January 1 2015 to January 1 2025. The data was gathered from the Yahoo Finance website, utilizing only the closing price ('close') variable. The research process included data pre-processing, Min-Max normalization, LSTM modeling with varying timesteps (30, 60, 90 days), and evaluation of prediction results. The LSTM model was built with two LSTM layers, a dropout layer, and a final dense layer, and its training involved the application of the mean_squared_error loss function and Adam optimizer. Evaluation results showed that the model configuration with 60 timesteps achieved optimal performance with a RMSE of 114.17, MAPE percentage of 0.96%, and an R-Squared of 0.98, indicating highly accurate and reliable predictions. This study demonstrated that LSTM is an effective model for stock price prediction based on time series data.
Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus Robby Azhar; Siska Kurnia Gusti; Iis Afrianty; Elvia Budianita
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.804

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected early; therefore, early diagnosis is highly important. One of the methods that can be applied for early diagnosis is the classification technique in data mining. However, the classification process often faces challenges due to class imbalance, which can reduce model performance. This study aims to analyze the effect of class balancing techniques on the performance of the Backpropagation Neural Network (BPNN) in classifying DM cases. BPNN is a form of Multi-Layer Perceptron (MLP) with a simple structure and the ability to solve complex problems with good accuracy. The dataset used in this study is the Pima Indians Diabetes Dataset, consisting of 768 instances, including 500 non-diabetic and 268 diabetic cases. The research was conducted using three scenarios: without balancing, Synthetic Minority Over-sampling Technique (SMOTE), and Random Under Sampling (RUS). The BPNN model was designed with two architectural variations (one hidden layer and two hidden layers), three learning rate values (0.1, 0.01, and 0.001), and a varying number of neurons. The dataset was divided using the 10-Fold Cross Validation technique. The results show that applying SMOTE achieved the best performance, with an average accuracy of 90.89%, precision of 91.22%, recall of 90.89%, and F1-score of 90.89% on the BPNN architecture with one hidden layer. Furthermore, the single hidden layer architecture proved more stable than the two hidden layers, especially when the dataset size decreased due to RUS. Therefore, the combination of SMOTE and BPNN with one hidden layer provides better performance in classifying Diabetes Mellitus cases.
Co-Authors Alwaliyanto Elvia Budianita Fadhilah Syafria Febi Yanto Iis Afrianty Kurniansyah, Juliandi Muhammad Affandes Robby Azhar