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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Jurnal Edukasi dan Penelitian Informatika (JEPIN) E-Dimas: Jurnal Pengabdian kepada Masyarakat AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Journal of Innovation and Community Engagement Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Inovatif Wira Wacana JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hapnes Toba
Department Of Master Of Computer Science, Faculty Of Information Technology Universitas Kristen Maranatha, Bandung, Indonesia
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Penentuan Aspek yang Berpengaruh Terhadap Produk Smartphone Berdasarkan Ulasan Berbasis Tekstual Hetthroh Sagala; Hapnes Toba
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.v7i1.3466

Abstract

With the rapid development of new technologies in smartphones, understanding market trends has become an increasingly difficult task. In these circumstances, online product reviews that can reflect consumer sentiment about the product has been a concern for now. Online review analysis can help sellers understand consumer interests and desires prior to launching a new product. The author wants to contribute to seeing the state of the smartphone market by creating a method to find important aspects of a smartphone. The data source comes from the Amazon e-commerce web with 4 predefined smartphone brands. In this study, the authors used topic modeling with the LDA algorithm and sentiment analysis with VADER to find aspects of a smartphone and its sentiment classification. From the 15 scenarios made in this reserach, it is found 3 aspects that always appear, namely the screen, camera, and battery aspects, so it is concluded that these 3 aspects are the most important of a smartphone based on textual reviews. Keywords— Topic Modeling; Sentiment Analysis; LDA; ecommerce
Segmentasi dan Pembentukan Model Regresi Nasabah Berbasis Analisis Recency, Frequency dan Monetary Ronaldo Cristover; Hapnes Toba; Bernard Renaldy Suteja
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.5075

Abstract

During this pandemic, the number of customers of a securities company has increased quite high. This requires securities companies to conduct analysis related to security customer data against transaction data so that the company can find out the segmentation of registered customers and also so that companies can predict the transaction patterns of customers in the company. In processing transaction data, the RFM (Recency, Frequency, Monetary) model can be used as a way to group customers according to their business values. After doing the modeling using RFM, the data is clustered using the K-Means algorithm to find out the segmentation in the RFM model in each group. The RFM model that has been clustered will produce segments based on the RFM group. In this data, a linear regression analysis process is carried out where each group and segment is analyzed and predicted related to variables such as recency, monetary and frequency. The results of data grouping, customer segmentation and also predictions with linear regression can be one of the company's references to make a business decision. From the linear regression process carried out on the RFM attributes, a prediction of the monetary value of the existing recency value is generated and the monetary value of the frequency can also be known with a fairly good error rate.
Ekstraksi Perilaku Komuter Pada Commuter Line Menggunakan Rule-Based Machine Learning Albertus Indarko Wiyogo; Setia Budi; Hapnes Toba
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.v9i1.6133

Abstract

The application of Automatic Fare Collection (AFC) on Commuter Line trains can provide new knowledge in navigating between Commuter Line train lines and real commuter travel data. The AFC system allows management to obtain large amounts of detailed data regarding the routes of each commuter daily. One of the challenges faced in using big data at AFC is the extraction of data on the behavior of transporting passengers. Commuter Line passenger behavior is a very important factor for operators to make the right decision. This study uses the association rules method to extract AFC data to produce good information and understand Jabodetabek commuter behavior. The results showed that the association rules method could extract AFC data and produce strong association rules on commuter behavior.
Deteksi Tindak Kecurangan Penjualan di Perusahaan Distribusi Menggunakan Machine Learning Budi Wibowo Suhanjoyo; Hapnes Toba; Bernard Renaldy Suteja
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 2 (2023): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i2.6932

Abstract

The sales department of a distribution company is one of the places where fraud often occurs. This fraud occurs in various ways and causes massive losses for the company. These frauds have certain patterns. The patterns that occur in these practices are studied by the company's internal auditor experts. The experience of these experts is processed into a system called the Expert System. The support of a technology-based tool is needed in order to detect sales fraud early. The purpose of this research is to be able to provide benefits for companies with early detection of fraud in the sales department. At the time this research was conducted, researchers had not found similar research with the same object. In this research, a comparison of various machine learning algorithm models will be carried out with the aim of knowing whether using machine learning technology can help detect fraud with a high accuracy value. The algorithm method used is supervised learning method. The algorithm models to be compared are Decision Tree, K-Nearest Neighbor, Random Forest, SVM and Logistic Regression. It is expected that by using machine learning technology, fraud can be detected early, so that the level of loss and risk of sales can be minimized.
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.
Pemanfaatan Teknik Peramalan Data Deret Waktu pada Inventori Farmasi di Rumah Sakit Aziz Mu'min; Setia Budi; Hapnes Toba
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9352

Abstract

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.
PELATIHAN GURU DAN TANTANGAN BEBRAS 2024 UNTUK PENGENALAN COMPUTATIONAL THINKING DI BIRO BEBRAS MARANATHA Wijanto, Maresha Caroline; Toba, Hapnes; Ayub, Mewati; Karnalim, Oscar; Tan, Robby; Natasya, Rossevine Artha; Senjaya, Wenny Franciska; Adelia; Edi, Doro; Bunyamin, Hendra; Kasih, Julianti; Yulianti, Diana Trivena; Widjaja, Andreas; Johan, Meliana Christianti; Surjawan, Daniel Jahja; Zakaria, Teddy Marcus; Risal; Kandaga, Tjatur
Jurnal Abdimas Ilmiah Citra Bakti Vol. 6 No. 2 (2025)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v6i2.5237

Abstract

Pemahaman siswa terhadap konsep Computational Thinking (CT) masih tergolong rendah, sementara pengenalan terhadap CT menjadi krusial di era digital saat ini. Tantangan Bebras menjadi sarana edukatif yang efektif untuk memperkenalkan CT melalui berbagai soal (Bebras task) yang bersifat aplikatif dan menantang. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterlibatan siswa dalam CT melalui pembekalan guru dan pelaksanaan Tantangan Bebras 2024. Mitra kegiatan adalah guru dan siswa dari jenjang SD, SMP, dan SMA yang tergabung dalam Biro Bebras Maranatha. Metode yang digunakan meliputi lokakarya nasional, pelatihan guru, technical meeting, pelaksanaan Tantangan Bebras, dan evaluasi prestasi siswa. Hasil menunjukkan peningkatan partisipasi peserta sebanyak 4.429 siswa dari 136 sekolah, meningkat signifikan dibanding tahun sebelumnya. Sebanyak 165 siswa berhasil meraih peringkat 1–6, dengan sebagian besar berasal dari sekolah yang mengikuti Gerakan PANDAI. Evaluasi juga menunjukkan bahwa pembekalan guru efektif meningkatkan kesiapan dalam mengenalkan CT kepada siswa. Kegiatan ini menunjukkan bahwa kolaborasi antara pelatihan guru dan Tantangan Bebras dapat menjadi strategi efektif untuk memperluas pemahaman dan kemampuan siswa dalam CT.
Pembelajaran Computasional Thinking melalui Program Gerakan Pandai untuk Guru dan PKBM Ayub, Mewati; Wijanto, Maresha Caroline; Tan, Robby; Surjawan, Daniel Jahja; Toba, Hapnes; Christianti, Meliana; Edi, Doro; Bunyamin, Hendra; Adelia, Adelia; Risal, Risal
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 7 No 3 (2023): Agustus
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v7i3.13430

Abstract

Program Gerakan Pandai yang digagas oleh Bebras Indonesia dengan dukungan Google bertujuan untuk membuat guru mulai menjadi guru penggerak dalam menyemaikan dan menumbuh-kembangkan kemampuan Computational Thinking (CT). Melalui gerakan PANDAI ini, diharapkan guru mengenal CT dan memperkenalkan CT kepada para siswa, sehingga siswa dapat mengembangkan kemampuan  berpikir komputasional yang bersifat kritis dan kreatif. Biro Bebras Maranatha menjalankan program Gerakan Pandai dalam dua batch yang dimulai pada bulan September 2020 sampai dengan Desember 2021. Pelatihan guru  batch1 diikuti oleh 148 guru, sedangkan batch2 diikuti 394 guru. Indikator guru yang berhasil menerapkan kemampuan CT adalah guru yang melaksanakan  paling sedikit 4 sesi microteaching dalam dua semester. Guru yang tuntas melakukan microteaching untuk batch1 ada 110 orang (74%), dan batch2 ada 184 guru (47%), dengan persentase rata-rata 60.5% untuk seluruh batch. 
Co-Authors ADELIA Adelia Adelia Adelia Adelia, Adelia Albertus Indarko Wiyogo Andreas Widjaja Aziz Mu'min Bernard Renaldy Suteja Billy Susanto Panca Budi Wibowo Suhanjoyo Budiman, Ariel Elbert Chandra Ari Gunawan, Chandra Ari Christian Wibisono Cristover, Ronaldo Daniel Jahja Surjawan Deon Diamanta Diamanta, Deon Diana Trivena Yulianti Djoni Setiawan Doro Edi Doro Edi Feliks Victor Parningotan Samosir Fernando, Edward Hanafi Hendra Bunyamin Hendra Bunyamin Hetthroh Sagala Husada, Ivan Nathaniel Johan, Meliana Christianti Joshua, Ronaldo Juan Elisha Widyaya Julianti Kasih Julianti Kasih Julianti Kasih, Julianti Julio Narabel Liliawati, Swat Lie Lucky Surya Haryadi Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Metayani, Vanessa Mewati Ayub Mu’min, Aziz Narabel, Julio Nava, Vardina Oscar Karnalim, Oscar Panji Yudasetya Wiwaha Risal Risal Risal Risal Risal Robby Tan Rolando Vieri Ronaldo Cristover Rossevine Artha Nathasya Sagala, Hetthroh Sendy Ferdian Sujadi Setia Budi Sherly Santiadi Teddy Marcus Teddy Marcus Zakaria Teddy Marcus Zakaria Tiur Gantini Tjatur Kandaga Tjatur Kandaga Gautama Valentina, Ivana Wenny Franciska Senjaya Wijaya, Bryan Yolanda Trixie Hernita