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All Journal Patria : Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Ilmiah Citra Bakti (JAICB) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Andreas Widjaja
Universitas Kristen Maranatha
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Pelatihan dan Pendampingan Siswa SMA Santa Maria 1 Bandung dalam Persiapan Tantangan Bebras 2021 Mewati Ayub; Risal Risal; Andreas Widjaja; Nikita Rebeca Yemima Kolinug; Vanika Ipsandy
Patria : Jurnal Pengabdian Kepada Masyarakat Vol 4, No 2: September 2022
Publisher : Universitas Katolik Soegijapranata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/patria.v4i2.4167

Abstract

Kegiatan pengabdian masyarakat yang dilakukan Biro Bebras UK Maranatha merupakan pelatihan dan pendampingan siswa SMA Santa Maria 1 Bandung dalam menghadapi Tantangan Bebras 2021 untuk pertama kali. Pelatihan dilakukan secara daring melalui Google Meet selama bulan Oktober 2021, dengan pertemuan sekali dalam seminggu. Materi pelatihan bersumber dari soal Bebras yang dipakai pada Tantangan Bebras 2019 dan 2020, yang dapat diakses dari web latihan Bebras IPB. Peserta pelatihan terdiri dari 12 siswa SMA kelas 10, 11, dan 12. Berdasarkan kuesioner evaluasi yang diisi oleh peserta, peserta menyatakan pelatihan ini membantu mereka dalam mempersiapkan diri menghadapi Tantangan Bebras. Dari pra Tantangan yang dilakukan di akhir pelatihan, terdapat 64% siswa yang memperoleh nilai di atas 50, dengan nilai rata-rata 52.27. Siswa juga berpendapat bahwa soal Bebras yang menarik adalah soal yang terkait langsung dengan persoalan dalam kehidupan nyata
PELATIHAN COMPUTATIONAL THINKING UNTUK GURU SDK 6 BPK PENABUR BANDUNG MELALUI BEBRAS TASK DAN AKTIVITAS UNPLUGGED Mewati Ayub; Hendra Bunyamin; Oscar Karnalim; Robby Tan; Maresha Caroline Wijanto; Doro Edi; Julianti Kasih; Andreas Widjaja; Adelia; Meliana Christianti; Wenny Franciska Senjaya; Swat Lie Liliawati; Rossevine Artha Nathasya
Jurnal Abdimas Ilmiah Citra Bakti Vol. 5 No. 3 (2024)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v5i3.3799

Abstract

Konsep computational thinking (CT) diperlukan dalam dunia digital saat ini agar setiap orang dapat belajar dan bekerja secara cerdas. Untuk membangun kembali interaksi antar guru dan siswa yang terkendala pada saat pandemi Covid 19, maka interaksi yang efektif antar guru dan siswa dalam pembelajaran pasca pandemi dapat dilakukan dengan menerapkan CT dalam pembelajaran.  Pelatihan CT untuk guru-guru SDK 6 BPK Penabur dilakukan dengan tujuan agar setiap guru dapat menerapkan konsep CT dan aktivitas unplugged dalam pembelajaran yang bersifat interaktif. Pelatihan guru yang dilaksanakan secara luring pada 15 Maret 2024 dan 22 Maret 2024, diikuti oleh 30 orang peserta. Setelah materi konsep CT, Bebras task, dan aktivitas unplugged disampaikan, guru diberi tugas kelompok untuk membuat rencana penerapan CT dalam mata pelajaran serta membuat rencana aktivitas unplugged untuk membantu siswa dalam menerapkan CT dalam persoalan sehari-hari. Hasil dari tugas kelompok yang dibuat peserta menunjukkan nilai rata-rata sangat baik dalam penerapan CT dan aktivitas unplugged. Sebagian besar peserta berpendapat penerapan CT sangat bermanfaat untuk diterapkan dalam pembelajaran di tingkat sekolah dasar untuk melatih anak berpikir kritis dan kreatif dalam memecahkan masalah di kehidupan sehari-hari.
Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel Kristiawan Kristiawan; Andreas Widjaja
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3182

Abstract

Abstract — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location. Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.
Analisis Deret Waktu dari Produk yang Terjual Menggunakan Beberapa Teknik Populer Laras Ervintyana; Andreas Widjaja; Swat Lie Liliawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 1 (2023): JuTISI (in progress)
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i1.5933

Abstract

Sales is the most important component in and industrial company. This is because, income comes into the company if sales is running, therefore it is important to do an analysis of the products sold so that the company can prepare earlier before the demand for these products come, in order to produce better income. This study uses ARIMA, SVR, FFT and Prophet to forecast and with MAPE and RMSPE as the measure level of accuracy, also to see if there is any seasonality in the product that is being analyzed, seasonal_decompose is used. The results of the analysis show that ARIMA and Prophet are the best forecasting methods, this is because both methods have the lowest MAPE and RMSPE value. After being analyzed using seasonal_decompose, it was found that all of the products studied have a pattern that repeats itself at a certain time every quarter. For more further analysis, it was done by head-to-head comparison, where 20 product samples of each category was used. By this analysis it was clear that products in Category 1 are better to use ARIMA and products in Category 2 are better to use Prophet.
Perbandingan Akurasi Model Pembelajaran Mesin untuk Prediksi Seleksi Masuk Perguruan Tinggi Negeri Oktavianus Yopi Wardana; Mewati Ayub; Andreas Widjaja
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 1 (2023): JuTISI (in progress)
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i1.6126

Abstract

Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) is still one of the favorite admission routes for high school students to continue their education at Perguruan Tinggi Negeri (PTN). SNMPTN uses semester 1 to 5 report card scores for 6 subjects that are inputted in Pangkalan Data Sekolah dan Siswa (PDSS). Prediction of SNMPTN can be done using machine learning models with various methods. This study aims to create a predictive model using the Decision Tree CART, Gaussian Naïve Bayes and Logistic Regression methods, make predictions and compare the level of accuracy of the models made. The methodology used in this research is Knowledge Discovery in Database (KDD). This is to get useful knowledge from data. The dataset used is data on the scores of 6 subjects for 5 semesters from class 2015 to 2022. Model evaluation uses the Split Percentage Method and K-Fold Cross Validation. The results show that the accuracy scores for the 3 models are different. Logistic Regression has a score of 0.82, followed by Decision Tree CART with a score of 0.75 and finally Gaussian Naïve Bayes with a score of 0.70. The hypothesis put forward by the researcher is in accordance with the results obtained, that the Logistic Regression model has a higher accuracy score. Mathematically, Logistic Regression is not too complicated when compared to other models. To get a model that fits with needs must involve iterating through the machine learning process and trying various variations.
Co-Authors Adelia Adelia, Adelia Doro Edi Hendra Bunyamin Johan, Meliana Christianti Julianti Kasih Kristiawan Kristiawan Laras Ervintyana Liliawati, Swat Lie Maresha Caroline Wijanto Mewati Ayub Nikita Rebeca Yemima Kolinug Oktavianus Yopi Wardana Oscar Karnalim, Oscar Risal Risal Robby Tan Rossevine Artha Nathasya Vanika Ipsandy Wenny Franciska Senjaya