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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control jurnal teknik informatika dan sistem informasi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Oscar Karnalim
Universitas Kristen Maranatha
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Social Sciences

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The Use of Python Tutor on Programming Laboratory Session: Student Perspectives Oscar Karnalim; Mewati Ayub
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 4, November-2017
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3314.027 KB) | DOI: 10.22219/kinetik.v2i4.442

Abstract

Based on the fact that the impact of educational tools can only be accurately measured through student-centered evaluation, this paper proposes a long-term in-class evaluation for Python Tutor, a program visualization tool developed by Guo. The evaluation involves 53 students from 4 Basic Data Structure classes, which were held in the even semester of 2016/2017 academic year. It is conducted based on questionnaire survey asked to the students after they have used Python Tutor in their half of programming laboratory sessions. In general, there are three findings from this work. Firstly, Python Tutor helps students to complete programming laboratory tasks, specifically for Basic Data Structure material. Secondly, Python Tutor helps students to understand general programming aspects which are execution flow, variable content change, method invocation sequence, object reference, syntax error, and logic error. Finally, based on student perspectives, Python Tutor is a helpful tool positively affecting the students.
Pelatihan Guru untuk Tantangan Bebras 2022 di Biro Bebras Universitas Kristen Maranatha Mewati Ayub; Oscar Karnalim; Robby Tan; Maresha Caroline Wijanto; Doro Edi; Hendra Bunyamin; Julianti Kasih; Diana Trivena Yulianti; Andreas Widjaja; Risal Risal; Rossevine Artha Nathasya
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 14, No 3 (2023): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v14i3.14326

Abstract

Tantangan Bebras merupakan salah satu kegiatan yang memperkenalkan computational thinking dan informatika kepada siswa sekolah. Bebras Indonesia melalui setiap mitra biro Bebras di seluruh Indonesia menyelenggarakan Tantangan Bebras setiap tahunnya yaitu pada minggu kedua bulan November. Biro Bebras Maranatha juga mempersiapkan guru-guru yang berada di bawah naungan Biro Bebras Maranatha dalam kegiatan pelatihan pada 7 Oktober 2022 secara hybrid dan technical meeting pada 28 Oktober 2022. Pelatihan untuk tahun 2022 dimulai dengan kuis soal-soal Bebras yang diambil dari soal-soal dalam Tantangan Bebras tahun-tahun sebelumnya untuk mengukur tingkat pemahaman guru dalam computational thinking. Kegiatan pelatihan dilanjutkan dengan pembahasan soal kuis melalui diskusi, penyampaian konsep computational thinking, serta pendaftaran dan persiapan siswa untuk Tantangan Bebras 2022. Pada akhir sesi pelatihan, guru-guru peserta mengisi kuesioner untuk mengetahui sejauh mana persiapan yang sudah dilakukan untuk Tantangan Bebras 2022. Pelaksanaan kegiatan dilaksanakan secara hybrid diikuti oleh 52 guru perwakilan sekolah. Dari 52 guru yang mengikuti kuis, nilai kuis berkisar antara 0 sampai 80 di mana rata-rata nilai adalah 35. Sebanyak 79% dari guru-guru yang mengikuti pelatihan ini sudah pernah mengikuti workshop Bebras di tahun-tahun sebelumnya dan 69% dari total guru tersebut telah memanfaatkan soal Bebras untuk pembelajaran di kelas. Selama proses pembekalan Tantangan Bebras, terdapat tiga tantangan terbesar yang dihadapi yaitu kemampuan berpikir siswa, persiapan guru untuk pembekalan, dan melatih siswa dalam membaca soal.
Analisis Klaster Kriteria Gangguan Kecemasan Sosial Berdasarkan Fase Perawatannya Panji Yudasetya Wiwaha; Hapnes Toba; Oscar Karnalim
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 1 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i1.8400

Abstract

This study aims to cluster the activity dataset of patients who suffer from social anxiety disorder at a Mental Healthcare Company located in the Netherlands and measure the affinity of the cluster to the identified treatment phase based on the similarity of its feature density. The methodology of data clustering is carried out in the following way: 1) data pre-processing against the anonymous patient data, communication data, tracker data of the social anxiety disorder, registration history of the daily entry, notification data, planned event completion data, questionnaires related to the relevancy of the treatment, history of the patient's treatments, and registration history of the thought record, 2) exploratory data analysis to visualize the data point distribution of the activity dataset, perform data standardization, and find the optimal number of clusters, and 3) building a clustering model using the k-Means algorithm. The effectiveness of data clustering is validated by 1) comparing the affinity of clusters to the identified treatment phase and 2) calculating the feature weights to find any features with unique characteristics (dominant) in each treatment phase. The k-Means model successfully grouped the activity dataset into 10 clusters. The clusters are analyzed based on the pattern of cluster affinity and its percentage ratio. Then, 3 clusters are selected because they are close enough to represent each treatment phase in the Mental Healthcare Company. The findings in this study show that the number of days since the patient made a registration, the number of registrations related to social anxiety disorder in the past week, the comparison of negative registrations in the past week compared to one week before, questionnaire scores related to treatment relevancies, and low scores in any questionnaire indicators are distinguished features for each treatment phase. In addition, the urgency of those features matches the therapist's top priority list when treating their clients. Nonetheless, further and comprehensive research must be conducted to understand the impact of the dominant features in each cluster so the classification model for creating a list of recommended patients based on their urgency level of treatment can be built.
Topic Analysis Video Debat Jelang Pemilu Presiden dan Wakil Presiden Tahun 2024 Ivana Valentina; Aziz Mu’min; Devion Tanrico; Oscar Karnalim
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 12 No 1 (2024): TEKNOIF APRIL 2024
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2024.V12.1.29-35

Abstract

Several debates were held among presidential and vice presidential candidates to convey their ideas for the 2024 presidential general election (PEMILU). This research analyzes the topics discussed in the debates using Latent Dirichlet Allocation (LDA), K-means Clustering, and word tagging methods for each candidate pair. The K-Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate A. The K- Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate. These are somewhat consistent with previos works. A. In dataset 1 using the LDA model, candidate pairs A have a probability of 60%, B have a probability of 25%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 37.04%, B have a probability of 25%, and C have a probability of 17.24%. In dataset 2 using the LDA model, candidate pairs A have a probability of 100%, B have a probability of 40%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 35.71%, B have a probability of 14.29%, and C have a probability of 28.57%.
Co-Authors Andreas Widjaja Aziz Mu’min Devion Tanrico Diana Trivena Yulianti Doro Edi Hapnes Toba Hendra Bunyamin Ivana Valentina Julianti Kasih Maresha Caroline Wijanto Mewati Ayub Panji Yudasetya Wiwaha Risal Risal Robby Tan Rossevine Artha Nathasya