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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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Implementasi Sistem Tanya Jawab Berbasis Skenario untuk Mendukung Proses Akademik dengan IBM Watson Assistant Toba, Hapnes; Wijaya, Bryan
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 2 (2020): Volume 6 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i2.40715

Abstract

Dalam makalah ini disampaikan sebuah hasil penelitian dengan memanfaatkan teknologi dari IBM, yaitu Watson Assistant. Watson Assistant digunakan untuk membuat chatbot terkait proses akademik. Analisis dan pengumpulan data dilakukan dengan berbasiskan skenario. Data-data tersebut dibuat ke dalam sebuah graph search. Watson Assistant akan menentukan node dengan nilai kepercayaan tertinggi untuk diberikan sebagai jawaban. Skenario percakapan yang ditanamkan dalam chatbot ini telah diimplementasikan ke dalam bentuk laman web, Facebook Messenger, dan Slack untuk membantu interaksi antara pihak fakultas dengan mahasiswa. Chatbot berperan pula sebagai sistem pendamping forum tanya jawab di dalam course learning system (CLS) untuk pertanyaan-pertanyaan rutin. Berdasarkan hasil uji coba, chatbot berbasis skenario telah dapat menjawab kebutuhan dasar mahasiswa untuk bertanya seputar hal akademis, sebagaimana tercantum dalam buku panduan, khususnya untuk proses perwalian dan deskripsi mata kuliah.
Service Learning in Teachers and Students Mentoring for 2020 Bebras Challenge in Pandemic Era at Maranatha Bebras Bureau Christian University Mewati Ayub; Maresha Caroline Wijanto; Adelia Adelia; Billy Susanto Panca; Doro Edi; Julianti Kasih; Hapnes Toba; Risal Risal; Meliana Christianti; Robby Tan; Daniel Jahja Surjawan
Journal of Innovation and Community Engagement Vol. 2 No. 2 (2021)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jice.v2i2.3802

Abstract

Bebras Challenge is a competition for elementary to high school students to educate informatics and computational thinking, followed by sixty countries all over the world. Bebras Indonesia Community in coordination with the International Bebras Committee holds the challenge yearly. Indonesia has participated in the Bebras Challenge since 2016. Faculty of Information Technology Maranatha Christian University as a Bebras Bureau has also been involved in the challenge since 2016. To prepare students for Bebras Challenge, Maranatha Bebras Bureau holds a teacher workshop yearly. The Teacher Workshop supports teachers to strengthen students in practicing Bebras tasks. Data on students who participated in the Bebras Challenge at Maranatha Bebras Bureau indicates increasing numbers from 2016 until 2020. This paper describes a service learning for mentoring teachers and students in the Bebras Challenge, which was held in the pandemic year 2020. Teacher mentoring was using a service learning approach, where the lecturers provided training to the teachers and then the teachers would share their knowledge back to their students. There were advantages and disadvantages of the execution during the pandemic. Although in a distance learning condition, teachers and students were still enthusiastic to participate in Bebras Challenge. The number of students who followed the 2020 Bebras Challenge nearly five times compared to 2019 in the Maranatha Bebras Bureau. The scores of elementary school students who followed the challenge showed very good results. On the other side, the results of junior and senior high school students were not as good as the scores of elementary school students.
PENYULUHAN PENGENALAN DUNIA DIGITAL MARKETING BAGI DESA CIBODAS Hendra Bunyamin; Julianti Kasih; Tiur Gantini; Teddy Marcus; Hapnes Toba; Djoni Setiawan; Sherly Santiadi; Rolando Vieri
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 5 No 3 (2022): APTEKMAS Volume 5 Nomor 3 2022
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v5i3.4664

Abstract

The social media revolution has completely changed consumer behavior as well as the Internet. A marketing strategy which reaches consumers through internet usage channels, mobile devices, search engines, and social media in the broadband Internet corridor is needed by businessmen nowadays. This marketing strategy so-called digital marketing is required to compete for customers in the 21st century. Specifically in this community dedication project, we deliver a webinar to educate villagers from Desa Cibodas about digital marketing skills to create business opportunities by utilizing digital platforms. Additionally, this webinar is a part of Digital Village program which is a joint cooperation between Dinas Komunikasi dan Informatika Provinsi Jawa Barat and Asosiasi Pendidikan Tinggi Informatika dan Komputer (APTIKOM). Keywords: community dedication, digital marketing, entrepreneurship
Masking preprocessing in transfer learning for damage building detection Hapnes Toba; Hendra Bunyamin; Juan Elisha Widyaya; Christian Wibisono; Lucky Surya Haryadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp552-559

Abstract

The sudden climate change occurring in different places in the world has made disasters more unpredictable than before. In addition, responses are often late due to manual processes that have to be performed by experts. Consequently, major advances in computer vision (CV) have prompted researchers to develop smart models to help these experts. We need a strong image representation model, but at the same time, we also need to prepare for a deep learning environment at a low cost. This research attempts to develop transfer learning models using low-cost masking pre-processing in the experimental building damage (xBD) dataset, a large-scale dataset for advancing building damage assessment. The dataset includes eight types of disasters located in fifteen different countries and spans thousands of square kilometers of satellite images. The models are based on U-Net, i.e., AlexNet, visual geometry group (VGG)-16, and ResNet-34. Our experiments show that ResNet-34 is the best with an F1 score of 71.93%, and an intersection over union (IoU) of 66.72%. The models are built on a resolution of 1,024 pixels and use only first-tier images compared to the state-of-the-art baseline. For future orientations, we believe that the approach we propose could be beneficial to improve the efficiency of deep learning training.
Pembelajaran Computasional Thinking melalui Program Gerakan Pandai untuk Guru dan PKBM Mewati Ayub; Maresha Caroline Wijanto; Robby Tan; Daniel Jahja Surjawan; Hapnes Toba; Meliana Christianti; Doro Edi; Hendra Bunyamin; 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. 
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums Hapnes Toba; Yolanda Trixie Hernita; Mewati Ayub; Maresha Caroline Wijanto
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i1.26024

Abstract

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.
Evaluasi Metodologi CI/CD untuk Pengembangan Perangkat Lunak dalam Perkuliahan Hapnes Toba; Tjatur Kandaga Gautama; Julio Narabel; Andreas Widjaja; Sendy Ferdian Sujadi
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 2 (2022): Volume 8 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i2.51992

Abstract

Saat ini sistem Continuous Integration (CI)/ Continuous Delivery (CD) merupakan standar baru dalam pengembangan perangkat lunak di industri. Sistem CI/CD merupakan langkah otomatisasi dari sebagian proses dalam pengembangan perangkat lunak. Ketika suatu sistem CI/CD digunakan oleh tim pengembang perangkat lunak maka akan diperoleh banyak data pemrosesan dan data hasil akhir dari proses CI/CD tersebut. Penelitian ini berupaya untuk mengevaluasi data yang terhimpun dalam sebuah sistem CI/CD dan diharapkan akan menemukan informasi yang bermanfaat sebagai umpan balik terhadap potensi sistem CI/CD dalam perkuliahan.  Evaluasi riset dilakukan dengan metode survei pada kelas pilihan di semester ganjil tahun akademik 2021/22. Survei dimulai sejak masa ujian tengah semester sampai dengan akhir semester, yaitu pada saat mahasiswa peserta kelas mulai membuat sistem/ aplikasi guna memenuhi kelengkapan tugas besar mata kuliah. Adapun kelas yang dipilih tersebut adalah mata kuliah rekayasa perangkat lunak di program studi S-1 Teknik Informatika. Hasil survei menunjukkan bahwa mayoritas mahasiswa sangat antusias dan merasa penting untuk mendalami konsep CI/CD sebagai salah satu metode mutakhir pengembangan perangkat lunak.
Ekstraksi Perilaku Pasien Pada Kunjungan Poliklinik Rumah Sakit Menggunakan FP-Growth Liliawati, Swat Lie; Toba, Hapnes; Ayub, Mewati; Mu’min, Aziz; Valentina, Ivana; Metayani, Vanessa; Nava, Vardina
Jurnal Inovatif Vol. 2 No. 3 (2023): Desember 2023
Publisher : Jurnal Inovatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58300/inovatif-wira-wacana.v2i3.681

Abstract

Penerapan sistem informasi management rumah sakit (SIMRS) pada sebuah rumah sakit dapat memberikan pengetahuan baru dalam melakukan pengelolaan rumah sakit dan memungkinkan manajemen rumah sakit untuk memperoleh data pasien dalam jumlah besar mengenai kunjungan pasien. Salah satu tantangan dalam menggunakan big data di rumah sakit adalah ekstraksi perilaku pasien dalam melakukan kunjungan ke poliklinik di rumah sakit. Perilaku kunjungan pasien ini merupakan faktor yang sangat penting bagi pihak management rumah sakit untuk mengambil keputusan yang tepat. Dalam penelitian ini menggunakan metode association rules untuk mengekstrak data kunjungan pasien agar dapat menghasilkan informasi yang baik dan dapat dipahami perilaku kunjungan pasien di rumah sakit. Hasil penelitian ini menunjukan bahwa dengan metode association rules dapat mengekstraksi data kunjungan pasien dan menghasilkan aturan asosiasi yang kuat pada perilaku kunjungan pasien.
Pengembangan Computational Thinking Siswa melalui Tantangan Bebras 2023 di Biro Bebras Universitas Kristen Maranatha Mewati Ayub; Robby Tan; Maresha Caroline Wijanto; Rossevine Artha Nathasya; Adelia Adelia; Wenny Franciska Senjaya; Oscar Karnalim; Daniel Jahja Surjawan; Doro Edi; Hapnes Toba; Meliana Christianti; Julianti Kasih; Risal Risal; Diana Trivena Yulianti; Teddy Marcus Zakaria; Swat Lie Liliawati
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 3 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i3.18162

Abstract

Pengabdian masyarakat yang dilakukan bertujuan untuk mengembangkan kemampuan Computational Thinking (CT) siswa melalui kegiatan Tantangan Bebras. Tantangan Bebras adalah kegiatan untuk memberi tantangan kepada siswa berupa sekumpulan Bebras task yang harus diselesaikan dalam waktu terbatas. Bebras task mengandung konsep Computational Thinking dan informatika yang dikemas dalam bentuk persoalan yang harus dipecahkan. Tantangan Bebras diadakan oleh Bebras Indonesia setiap tahun pada minggu kedua bulan November dengan melibatkan mitra Biro Bebras di seluruh Indonesia. Biro Bebras Universitas Kristen Maranatha mempersiapkan guru pendamping siswa melalui pelatihan guru agar dapat membimbing siswa dalam berlatih memecahkan Bebras task. Dalam pelatihan, guru diperkenalkan dengan Bebras task melalui kuis yang kemudian dibahas bersama. Guru juga diberi materi pengenalan CT dan aktivitas unplugged. Masa pendaftaran peserta Tantangan Bebras dilakukan setelah pelatihan, pendaftaran dilakukan secara kolektif melalui sekolah. Ada 4 kategori lomba, yaitu SiKecil untuk SD kelas 1-3, Siaga untuk SD kelas 4-6, Penggalang untuk SMP, dan Penegak untuk SMA. Terdapat 54 sekolah yang mendaftarkan siswanya. Menjelang hari Tantangan diadakan technical meeting untuk guru sebagai persiapan untuk mendampingi siswa pada saat uji coba akun dan pada saat tantangan. Peserta yang mengikuti Tantangan melalui Biro Bebras UK Maranatha berjumlah 3429 orang, yang terbanyak adalah kategori Penggalang. Hasil Tantangan menunjukkan kategori Siaga dan SiKecil sudah baik, sedangkan kategori Penggalang dan Penegak perlu mempersiapkan diri lebih baik di tahun mendatang.
Pendeteksian Citra Pengunjung Menggunakan Single Shot Detector untuk Analisis dan Prediksi Seasonality Deon Diamanta; 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.v7i1.3329

Abstract

This study discusses the analysis of retail store with time series method to obtain information about sales trend and seasonality by looking at visitor data and total transaction data at a time period. Data in the form of the number of customers who visit are obtained through CCTV video camera recordings placed at retail store X and the total transaction occurred at retail store X. The visitor counting uses the deep learning method with SSD (Single Shot Detector) object detection framework and MobileNet architecture. The library used to count the number of customers visiting the store is OpenCV, Pandas, Numpy, Dlib, and Imutils. The number of customers visiting the store will then be compared to the number of transactions that occur at the same time so that a conversion rate can be obtained. From here, we can see sales trend that occur at any time. Time series analysis is also carried out to determine and analyze the pattern of data obtained based on certain time to predict the things that need to be done in the future. Through this research, information has been successfully obtained related to seasonality patterns, value and interpretation of retail conversion rates, models for predicting the number of visitors and transactions, and answering the hypothesis with the Wilcoxon test method obtained a p-value of 0,014 which states that the data pattern of the number of consumers is not the same as the transaction data pattern.
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