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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Terpadu International Journal for Educational and Vocational Studies Journal of Sustainable Engineering: Proceedings Series Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Sistem Informasi Jurnal Teknik Elektro dan Komputer Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Daniel Febrian Sengkey
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sam Ratulangi, Jln. Kampus Unsrat, Bahu - Manado 95115, INDONESIA
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Correlations between Online Learning Media Types, First Access Time, Access Frequency, and Students’ Achievement in a Flipped Classroom Implementation Daniel Febrian Sengkey; Sary Diane Ekawati Paturusi; Alwin Melkie Sambul
Jurnal Sistem Informasi Vol. 17 No. 1 (2021): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.932 KB) | DOI: 10.21609/jsi.v17i1.1008

Abstract

Since the 1960s, the world has seen how Information Technology (IT) influences education. In the present era, with the massive development of the Internet, various kinds of IT-assisted learning are popping up like mushrooms in the rainy season. However, no matter how advanced IT-assisted learning has been grown, learning media is still an inseparable part of education. In this study, we specifically present how the use of certain types of learning media correlated with students’ access behaviors and, more importantly, students’ achievement. The result shows that these factors have a positive correlation. In terms of media type influence towards students’ achievement, the media that has the appearance of the lecturer gives better achievement, compared to the media that only has audio, and the media that only consists of text and images.
Penilaian Mahasiswa terhadap Jenis Media Pembelajaran dalam Penerapan Flipped Classroom Daniel F. Sengkey; Alwin M. Sambul; Sary D.E. Paturusi
Jurnal Teknik Elektro dan Komputer Vol. 8 No. 2 (2019): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v8i2.25029

Abstract

Abstract — Due to the rapid advancement of Information Technology, new ways of living are also developed. Learning styles are also affected. Internet proliferation in the last few decades has triggered the increasing adoption of online and blended learning. This article discusses students’ assessment to learning media in a flipped classroom application. There were 3 media studied: text and images, recorded slide show with audio narration, and recorded slide with the appearance of the lecturer within the frame. The results show that students are tend to give higher scores for the slide-based media, compared with the media that only consists with text and images. Keywords — blended learning; flipped-classroom; learning media; online learning Abstrak — Dengan pesatnya perkembangan Teknologi Informasi, maka berkembang berbagai cara baru bagi manusia dalam menjalani kehidupan, termasuk di dalamnya cara manusia belajar. Perkembangan Internet dalam beberapa dekade terakhir telah membuat adopsi pembelajaran daring (online) dan pembelajaran campuran (blended) semakin meningkat. Artikel ini membahas tentang penilaian mahasiswa terhadap media pembelajaran dalam penerapan flipped-classroom. Ada tiga jenis media yang digunakan, yaitu teks dan gambar (citra), rekaman slide dengan narasi suara, dan yang terakhir, rekaman slide dengan tampilan dosen yang sementara menjelaskan materi. Hasil penelitian menunjukkan bahwa mahasiswa cenderung memberikan penilaian yang lebih baik terhadap media rekaman slide dibandingkan dengan media yang hanya berisikan teks dan gambar saja.
Perbandingan Akses Mahasiswa terhadap Media Pembelajaran Daring dalam Penerapan Flipped Classroom Daniel Febrian Sengkey; Sary Diane Ekawati Paturusi; Alwin Melkie Sambul
Jurnal Teknik Elektro dan Komputer Vol. 9 No. 1 (2020): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v9i1.28634

Abstract

Meskipun media pembelajaran sangat massif digunakan dalam pendidikan berbasis Teknologi Informasi, tetapi penelitian-penelitian tentang media pembelajaran masih menyisakan berbagai macam pertanyaan terkait penggunaan, preferensi mahasiswa dan efikasinya. Penelitian ini membahas 3 jenis media yang digunakan dalam 2 mata kuliah yang menerapkan flipped classroom. Media-media tersebut adalah teks dan gambar, rekaman slideshow dengan suara dari pengajar, dan rekaman slideshow dengan tampilan dari pengajar yang sementara menjelaskan. Artikel ini berfokus pada perbandingan jumlah akses serta durasi dari akses perdana relative terhadap jadwal kuliah dari ketiga media yang diteliti. Hasil penelitian ini menemukan media dengan teks dan gambar sebagai media yang relatif sering diakses. Di sisi lain, media dengan rekaman audio menjadi media yang umumnya diakses lebih awal dibandingkan kedua jenis media pembelajaran lainnya.
Implementasi Plugin STACK pada Sistem e-Learning UNSRAT: Implementing the STACK Plugin on the Sistem e-Learning UNSRAT Daniel Febrian Sengkey
Jurnal Teknik Elektro dan Komputer Vol. 12 No. 2 (2023): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v12i2.48562

Abstract

Assessment is an integral part of education. Through assessment, instructors may evaluate the learners' achievement. In a large class setup, holding an assessment and checking the learners' work leads to a tiresome job if done manually. Therefore, the use of an automated procedure, such as a computerized solution is advised. However, the question types that can be automatically checked does not cover essay question. It is very unfortunate for mathematical-based courses where evaluating the reasoning of the learners is essential, hence adopting question types such as Multiple Choice Questions, True/False; or even Numeric, which ask the learner to input a single number as an answer, would be inadequate. In this paper, we describe the implementation of the STACK, a MOODLE plugin that supports the assessment of mathematical expressions. The preliminary results show that with the correct way of introducing the commands to the learners, instructors can author questions with adequate discrimination index and efficiency. Penilaian merupakan bagian integral dari pendidikan. Melalui penilaian, instruktur dapat mengevaluasi prestasi peserta didik. Dalam pengaturan kelas besar, mengadakan penilaian dan memeriksa hasil kerja siswa akan menjadi pekerjaan yang melelahkan jika dilakukan secara manual. Oleh karena itu, disarankan untuk menggunakan prosedur otomatis, seperti solusi terkomputerisasi. Namun, jenis soal yang dapat diperiksa secara otomatis tidak mencakup soal esai. Sangat disayangkan untuk kursus berbasis matematika di mana mengevaluasi penalaran peserta didik sangat penting, sehingga mengadopsi jenis pertanyaan seperti Pertanyaan Pilihan Ganda, Benar/Salah; atau bahkan Numerik, yang meminta pembelajar untuk memasukkan satu angka sebagai jawaban, tidak akan memadai. Dalam makalah ini, kami menjelaskan implementasi STACK, sebuah plugin MOODLE yang mendukung penilaian ekspresi matematika. Hasil awal menunjukkan bahwa dengan cara yang benar dalam memperkenalkan perintah kepada peserta didik, instruktur dapat menulis pertanyaan dengan indeks pembedaan dan efisiensi yang memadai.
Implementasi Bi-LSTM dengan Ekstraksi Fitur Word2Vec untuk Pengembangan Analisis Sentimen Aplikasi Identitas Kependudukan Digital Onsu, Romario; Sengkey, Daniel Febrian; Kambey, Feisy Diane
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1225

Abstract

The Indonesian government is striving to enhance digital public services, including the Digital Identity Application (IKD) launched in 2022 by the Directorate General of Population and Civil Registration. Since its launch, IKD has received various responses from the public. User reviews on Google Play Store indicate a decline in ratings from June to December 2023. Review analysis is essential to understand user satisfaction, identify issues, and guide application improvements. This study aims to perform sentiment analysis on IKD user reviews using Bidirectional Long Short-Term Memory (Bi-LSTM) and Word2Vec methods. Bi-LSTM and Word2Vec are used to develop sentiment analysis from previous research that still used Machine Learning methods. This research is expected to contribute to the development of sentiment analysis models using Deep Learning for the IKD application. Review data was collected from the Google Play Store using scraping techniques for the period January-December 2023 and categorized into positive and negative. The Bi-LSTM model was trained with Word2Vec CBOW and Skip-Gram variations with dimensions of 100, 200, and 300. The results show that the combination of Bi-LSTM and Word2Vec CBOW with a dimension of 200 and a data split ratio of 80/20 produced the highest accuracy of 96.06%, with a precision of 96.44%, recall of 95.64%, and an f1-score of 96.04%. All combinations of Bi-LSTM and Word2Vec outperformed other Machine Learning algorithms.
Regression Algorithms in Predicting the SARS-CoV-2 Replicase Polyprotein 1ab Inhibitor: A Comparative Study Sengkey, Daniel Febrian; Masengi, Angelina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.338

Abstract

Due to its extensive steps and trials, drug discovery is a long and expensive process. In the last decade, as also hard pressed by the COVID-19 pandemic, the screening process could be assisted with the advancement in computational technology including the application of Machine Learning. The classification task in Machine Learning has become one of the major approaches for drug discovery. Unfortunately, this practice uses discretized labels that might lead to the loss of quantitative properties that could be meaningful. Therefore, in this paper, we aim to compare various Machine Learning regression algorithms in predicting inhibitory bioactivity, specifically the IC50 value, with the SARS-CoV-2 Replicase Polyprotein 1ab as the target. With 1,138 non-duplicated data downloaded from the ChEMBL database that was engineered into four dataset variances, 42 regression algorithms were utilized for the prediction. We found that there are computational challenges to the use of regression algorithms in predicting bioactivity, for only a handful and a specific dataset variance that returned valid performance parameters upon testing. The three that yielded the highest counts of valid performance parameters are the Histogram Gradient Boosting Regressor (HGBR), Light Gradient Boosting Machine Regressor (LGBR), and Random Forest Regression (RFR). Further statistical analyses show that there is no significant difference between these three algorithms, except for the time taken for training and testing the model, where the LGBR excels. Therefore, these three algorithms should be primarily considered for the study with the same nature.
Comparative Analysis of Hepatitis C virus Genotype 1a (Isolate 1) using Multiple Regression Algorithms and Fingerprinting Techniques Nur Fiat, Daffa; Suratinoyo, Syifabela; Kolang, Indri Claudia; Ticoalu, Injilia Tirza; Purnomo, Nadira Tri Ardianti; Mawara, Reza Michelly Cantika; Sengkey, Daniel; Masengi, Angelina Stevany Regina; Sambul, Alwin Melkie
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.506

Abstract

Approximately 70 million people worldwide have been infected with Hepatitis C virus (HCV), presenting a critical global health challenge. As a member of the Flaviviridae family, HCV can cause severe liver diseases such as cirrhosis, acute hepatitis, and chronic hepatitis. The Hepatitis C virus (HCV) genome encodes a single polyprotein consisting of 3010 amino acids, which when processed contains 10 polypeptides derived from cellular and viral proteases. These include structural proteins such as core protein, E1 and E2 envelope glycoproteins, and nonstructural proteins such as NS1, NS2, NS3, NS4A, NS4B, NS5A, and NS5B. Nonstructural proteins will be released by HCV NS2-3 and NS3-4A proteases, however, structural proteins will be released by host ER signaling peptidases. co-translationally and post-translationally form 10 individual structural proteins: 5'-C-E1-E2-p7-NS2-NS3-NS4A-NS4B-NS5A-NS5B-3'. Despite extensive research, there are significant gaps in predictive and analytical approaches to managing HCV, particularly in understanding the polyprotein structure and its implications for drug discovery. This study addresses these gaps by employing machine learning techniques to analyze HCV polyprotein using various fingerprinting methods and regression algorithms. The data was sourced from the ChEMBL database, and fingerprinting techniques such as PubChem, MACCS, and E-State were utilized. Regression algorithms, including Gradient Boosting Regression (GBR), Random Forest Regression (RFR), AdaBoost Regression (ABR), and Hist Gradient Boosting Regression (HSR), were applied. Model performance was evaluated using R² and Adjusted R² metrics, comparing default models with those enhanced by hyperparameter tuning. Feature importance analysis was conducted to identify key features influencing model performance, aiding in model simplification. The results show that although hyperparameter tuning does not significantly improve the predictive power of a model, it can provide an insight into model optimization. In particular, the default model showed higher R² and Adjusted R² values across different fingerprinting techniques compared to models with hyperparameterized features. Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) consistently performed well, with GBR showing the highest R² values when using PubChem fingerprints. Although there was no significant improvement through hyperparameter tuning, this study was able to find out the features that strongly influenced the model performance by conducting a feature importance analysis. This analysis helped simplify the model and highlighted the potential of machine learning in improving the understanding of HCV polyprotein structure. This research identifies optimal regression models and fingerprinting techniques, providing a strong framework for future drug discovery efforts aimed at improving global health outcomes. The research also shows that it is important to date to advance drug discovery using machine learning.
Pengembangan Aplikasi Berbasis Android Untuk Pembelajaran Dan Simulasi Test Kemampuan Berbahasa Inggris : Development of Android-Based Application For Learning and Simulation of English Language Proficiency Test Balansa, Kirsten; Paturusi , Sary D. E.; Sengkey, Daniel F. Sengkey
Jurnal Teknik Informatika Vol. 19 No. 03 (2024): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.v19i3.52932

Abstract

Abstrak — IELTS adalah ujian keterampilan bahasa Inggris internasional yang digunakan untuk mengukur kualitas bahasa Inggris seseorang yang akan menempuh pendidikan di luar negeri. Namun, masih banyak pelajar di Indonesia yang berkeinginan untuk belajar di luar negeri memiliki nilai IELTS yang masih di bawah 7. Seiring dengan perkembangan zaman yang semakin canggih, banyak hal yang dapat dilakukan untuk menyederhanakan segala sesuatu yang terkait dengan informasi, dengan tujuan membuat aplikasi ini sebagai media pembelajaran agar pelajar bisa dengan mudah belajar IELTS. Oleh karena itu, dibuatlah aplikasi pembelajaran interaktif berbasis Android untuk meningkatkan pemahaman siswa di Golden Gate English Center tentang IELTS Band 7. Aplikasi pembelajaran dan simulasi ini dibuat menggunakan metode Prototype. Berdasarkan hasil dan pengujian yang dilakukan, 75% responden yang menggunakan aplikasi setuju bahwa aplikasi pembelajaran ini sangat membantu mereka yang belajar IELTS. Sekitar 66,7% responden setuju bahwa aplikasi ini meningkatkan pemahaman mereka tentang IELTS, dan 83,3% menunjukkan minat yang signifikan untuk menggunakan aplikasi ini dibandingkan dengan belajar melalui buku atau buku teks. Selain itu, 75% pelajar setuju bahwa aplikasi ini lebih efektif dalam meningkatkan minat belajar IELTS.
Aplikasi Kecerdasan Artifisial Generatif Untuk Asesmen Pembelajaran Berdasarkan Rubrik: An Application of Generative Artificial Intelligence for Automated Rubric-Based Grading Legi, Moudy; Sengkey, Daniel Febrian; Sambul, Alwin Melkie
Jurnal Teknik Informatika Vol. 19 No. 03 (2024): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.v19i3.53916

Abstract

Abstract — Improving the effectiveness of learning assessments is essential in modern education. Therefore, the use of rubrics as an assessment tool has become crucial. This research aims to develop a web application that facilitates the learning assessment process using rubrics. The main issues faced in conventional assessment processes are inefficiency, especially in providing accurate and prompt feedback to learners, and the time-consuming process of creating rubrics. Hence, a solution is needed to enhance the assessment process by leveraging technology. The system development method used is the System Development Life Cycle (SDLC) with an Agile approach. The web application is built using the Flask framework, with integration of OpenAI’s ChatGPT to support automated assessment using rubrics. This application is designed to assist teachers in creating, saving, and managing rubrics efficiently. The research involves stages of needs analysis, design, development, testing, and evaluation. The developed web application successfully provides a solution to enhance the learning assessment process. Features such as automatic rubric creation and automated assessment improve assessment efficiency. This research makes a positive contribution to the development of technology in the context of learning and assessment. Keywords — artificial intelligence; chatgpt; flask; generative artificial intelligence; rubric assessment; web                 Abstrak — Meningkatkan efektivitas penilaian pembelajaran sangat penting dalam Pendidikan modern. Oleh karena itu, penggunaan rubrik sebagai alat penilaian telah menjadi hal yang krusial. Penelitian ini bertujuan untuk mengembangkan aplikasi yang memfasilitasi proses penilaian pembelajaran menggunakan rubrik. Masalah utama yang dihadapi dalam proses penilaian konvensional adalah ketidakefisienan, terutama dalam memberikan umpan balik yang akurat dan cepat kepada peserta didik, serta proses pembuatan rubrik yang memakan waktu. Oleh karena itu, diperlukan Solusi untuk meningkatkan proses penilaian dengan memanfaatkan teknologi. Metode pengembangan sistem yang digunakan adalah Siklus Hidup Pengembangan Sistem dengan pendekatan Agile. Aplikasi ini dibangun menggunakan kerangka kerja Flask, dengan integrasi ChatGPT dari OpenAI untuk mendukung penilaian otomatis menggunakan rubrik. Aplikasi ini dirancang untuk membantu guru dalam membuat, menyimpan, dan mengelola rubrik secara efisien. Penelitian melibatkan tahapan analisis, desain, pengembangan, pengujian, dan evaluasi. Aplikasi yang dikembangkan berhasil memberikan Solusi untuk meningkatkan proses penilaian pembelajaran. Fitur-fitur seperti pembuatan rubrik otomatis dan penilaian otomatis meningkatkan efisiensi penilaian. Penelitian ini memberikan kontribusi positif terhadap pengembangan teknologi dalam konteks pembelajaran dan penilaian.               Kata Kunci — ChatGPT; Flask; kecerdasan artifisial; kecerdasan artifisial generatif; penilaian rubrik; web
Analisa dan Perancangan UI/UX Aplikasi Alfagift Menggunakan Metode Design Thinking: Analysis and Design of UI/UX of Alfagift Application Using Design Thinking Method Kaunang, Stephani Gabriela; Tulenan, Virginia; Sengkey , Daniel F.
Jurnal Teknik Informatika Vol. 19 No. 04 (2024): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jti.v19i04.54195

Abstract

Alfagift application is an e-commerce application that provides a variety of daily needs of the community that can make it easier for users to carry out the shopping process from home without having to go to the store. The problems experienced by users are the absence of features to track delivery status, limited access to contact the store so that the ordered product passes the supposed delivery hours. Based on these problems, the UI/UX of the Alfagift Application was designed using the Figma application by adding features that meet user needs and using the Design Thinking method which has 5 stages Empathize, Define, Ideate, Prototype, and Test. This research resulted in additional features the order tracking feature, contact the store through the Alfagift application, change phone numbers, delete shopping items, and change the layout on the application homepage. Based on the results that have been made, testing is carried out on 100 users with questionnaires and interviews. Through calculations using the System Usability Scale method, the results obtained with an average value of 80.75 with the Acceptable category and Excellent rating, which means that the designed interface design can be accepted by users.
Co-Authors Ade Yusupa Agustinus Jacobus Alicia A.E. Sinsuw Alwin M. Sambul Alwin M. Sambul Alwin M. Sambul, Alwin M. Alwin Melkie Sambul Alwin Melkie Sambul Angelina Stevany Regina Masengi Arie Lumenta Arthur Morits Rumagit Bachmid, Muhdad Balansa, Kirsten Edward Nangoy, Edward Fabian Johanes Manoppo Feisy Diane Kambey Gozali, Chyntia Theresa Gozali, Chyntia Theresa Heilbert Armando Mapaly Indahsari, Bunga Jimmy Posangi Joseph, Victor Florencia Ferdinand Kainde, Henry Valentino Florensius Kambey, Feisy Diane Kaunang, Stephani Gabriela Kenneth Yosua Rumawas Palilingan Kolang, Indri Claudia Legi, Moudy Lu'o, Natalia Intan Suryani Mamahit, Juliet Merry Eva Mambo, Christi Diana Marthen Theogives Lasut, Marthen Theogives Mawara, Reza Michelly Cantika Nur Fiat, Daffa Onsu, Romario Paturusi , Sary D. E. Paturusi, Sary Diane Ekawati Paturusi, Sary Diane Ekawati Pertiwi, Junita Maja Pinrolinvic Duadelfri Kurnialimki Manembu Purnomo, Nadira Tri Ardianti Rizal Sengkey Salaki, Reynaldo Joshua Salvius Paulus Lengkong salvius paulus lengkong Sambul, Alwin Melkie Sambul, Alwin Melkie Saraun, Astri Sary D.E. Paturusi Sary Diane Ekawati Paturusi Sary Diane Ekawati Paturusi Sary Diane Ekawati Paturusi Sherwin R.U.A. Sompie Sugiarso, Brave Angkasa Suratinoyo, Syifabela Ticoalu, Injilia Tirza TRINA EKAWATI TALLEI Unsratdianto Sompie, Sherwin Reinaldo Victor Tarigan Virginia Tulenan Warouw, Finny Yulianty Sanggelorang