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All Journal TEKNIK INFORMATIKA Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Pemikiran dan Pengembangan Sekolah Dasar (JP2SD) Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains JOIV : International Journal on Informatics Visualization IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Teaching and Learning in Elementary Education Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Psychology and Instructions Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Instructional Development Journal Journal of Applied Engineering and Technological Science (JAETS) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Aksara: Jurnal Ilmu Pendidikan Nonformal Jurnal Manajemen Informatika Jayakarta Journal for Lesson and Learning Studies Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Teknik Informatika Journal of Applied Business and Technology Jurnal Elementaria Edukasia
Dewi Nasien
Instititut Bisnis Dan Teknologi Pelita Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Increasing Trust in AI with Explainable Artificial Intelligence (XAI): A Literature Review Nasien, Dewi; Adiya, M. Hasmil; Anggara, Devi Willeam; Baharum, Zirawani; Yacob, Azliza; Rahmadhani, Ummi Sri
Journal of Applied Business and Technology Vol. 5 No. 3 (2024): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v5i3.193

Abstract

Artificial Intelligence (AI) is one of the most versatile technologies ever to exist so far. Its application spans as wide as the mind can imagine: science, art, medicine, business, law, education, and more. Although very advanced, AI lacks one key aspect that makes its contribution to specific fields often limited, which is transparency. As it grows in complexity, the programming of AI is becoming too complex to comprehend, thus making its process a “black box” in which humans cannot trace how the result came about. This lack of transparency makes AI not auditable, unaccountable, and untrustworthy. With the development of XAI, AI can now play a more significant role in regulated and complex domains. For example, XAI improves risk assessment in finance by making credit evaluation transparent. An essential application of XAI is in medicine, where more clarity of decision-making increases reliability and accountability in diagnosis tools. Explainable Artificial Intelligence (XAI) bridges this gap. It is an approach that makes the process of AI algorithms comprehensible for people. Explainable Artificial Intelligence (XAI) is the bridge that closes this gap. It is a method that unveils the process behind AI algorithms comprehensibly to humans. This allows institutions to be more responsible in developing AI and for stakeholders to put more trust in AI. Owing to the development of XAI, the technology can now further its contributions in legally regulated and deeply profound fields.
Automated Waste Classification Using YOLOv11 A Deep Learning Approach for Sustainable Recycling Nasien, Dewi; Adiya, M. Hasmil; Farkhan, Mochammad; Rahmadhani, Ummi Sri; Samah, Azurah A.
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.205

Abstract

The rapid increase in waste generation due to urbanization and population growth has necessitated more efficient waste management solutions. Traditional waste sorting methods rely on manual labor, which is time-consuming, error-prone, and inefficient at large scales. This paper proposes an automated waste classification system using YOLOv11, the latest iteration of the YOLO family, which is known for its high speed and accuracy in object detection. By leveraging a custom dataset containing 10,464 labeled waste images from various categories—such as biodegradable, plastic, metal, paper, and glass—this study trains and evaluates a deep learning model capable of real-time waste identification and categorization. Experimental results demonstrate that YOLOv11 achieves high detection accuracy, with an overall classification accuracy of 94% and a mean average precision (mAP) exceeding previous methods. The model effectively differentiates between various waste types, though some misclassifications occur, particularly between visually similar materials like transparent plastic and glass. Performance metrics, including precision and recall, indicate the robustness of the proposed system in real-world applications. This research highlights the potential of YOLOv11 for integration into smart waste management systems, such as automated sorting machines and AI-powered recycling bins, to enhance efficiency and reduce environmental impact. Future work will focus on optimizing model performance by incorporating additional training data, applying advanced image augmentation techniques, and exploring hybrid approaches such as texture analysis and spectral imaging to improve classification accuracy. The implementation of this technology is expected to streamline waste recycling processes, minimize contamination in recyclable materials, and contribute to sustainable waste management practices.
Menciptakan Collaborative Learning Guru dan Peserta Didik Melalui Aplikasi Padlet Pada Sekolah Menengah Atas Pekanbaru Jollyta, Deny; Nasien, Dewi; Nora Marlim, Yulvia; Gustientiedina, Gustientiedina; Adiya, M. Hasmil; Mukhsin, Mukhsin; Rahmadian Yuliendi, Rangga; Kamal, Ahmad; Hajjah, Alyauma; Johan, Johan
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 8, No 2 (2025): April 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i2.3682

Abstract

Collaborative Learning requires teachers and students to maintain an engaging learning environment at all times. Problems emerge when teachers, notably high school teachers in Pekanbaru, employ learning material that do not support this. Teachers' creativity is pushed to constantly update how they present materials and evaluate students' knowledge in order to foster a collaborative and pleasurable learning environment. This community service project will help Pekanbaru high school instructors create collaborative and real-time learning tools. The Community Service Team employed an observation strategy to get a sense of learning at Santa Maria High School, which served as an example school. The proposed solution is technologically based, making use of the Padlet application. The Community Service Team offers training methods on smartphones and computers to help people grasp Padlet. The community effort resulted in a polished Padlet that teachers may use to study with students at any time. It is intended that studying using the Padlet application would boost teacher innovation and student learning results at Santa Maria High School, as well as high schools around Pekanbaru.Keywords: Teacher; padlet; collaborative learning; learners;SMA. Abstrak: Pembelajaran Kolaboratif (Collaborative Learning) mengarahkan guru dan peserta didik dalam suasana belajar yang interaktif setiap saat. Permasalahan muncul pada saat media pembelajaran yang digunakan guru tidak mendukung hal tersebut, termasuk guru-guru Sekolah Menengah Atas (SMA) di Pekanbaru. Kreativitas guru ditantang untuk selalu memperbaharui cara penyampaian materi, cara mengevaluasi pemahaman peserta didik hingga penilaian, demi terciptanya suasana belajar yang kolaboratif dan menyenangkan. Kegiatan pengabdian ini bertujuan untuk membantu guru SMA di Pekanbaru dalam mempersiapkan bahan pembelajaran yang kolaboratif dan real time. Tim Pengabdian melakukan metode observasi untuk mendapatkan gambaran pembelajaran melalui SMA Santa Maria yang dijadikan sebagai sekolah sampel. Metode yang diusulkan berbasis teknologi melalui pemanfaatan aplikasi Padlet. Untuk memudahkan pemahaman Padlet, Tim Pengabdian menggunakan metode pelatihan, baik melalui komputer maupun smartphone. Hasil pengabdian adalah Padlet jadi yang dapat digunakan guru dalam pembelajaran bersama peserta didik setiap waktu. Diharapkan pembelajaran melalui aplikasi Padlet mampu meningkatkan kreativitas guru dan hasil belajar peserta didik SMA Santa Maria khususnya dan SMA di Pekanbaru umumnya.Kata kunci: guru; padlet; pembelajaran kolaboratif; peserta didik; SMA.
A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images Muhammad Rakha; Mahmud Dwi Sulistiyo; Dewi Nasien; Muhammad Ridha
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.4836

Abstract

Breast cancer is the main cause of death in women throughout the world. Early detection using ultrasound is very necessary to reduce cases of breast cancer. However, the ultrasound analysis process requires a lot of time and medical personnel because classification is difficult due to noise, complex texture, and subjective assessment. Previous studies were successful in ultrasound classification of breast cancer but required large computations and complex models. This research aims to overcome these shortcomings by using a lighter but more accurate model. We integrated the CBAM attention module into the MobileNetV2 model to improve breast cancer detection accuracy, speed up diagnosis, and reduce computational requirements. Gradient Weighted Class Activation Mapping (Grad-CAM) is used to improve classification explanations. Ultrasound images from two databases were combined to train, validate, and test this model. The test results show that MobileNetV2-CBAM achieves a test accuracy of 93%, higher than the complex models VGG-16 (80%), VGG-19 (82%), InceptionV3 (80%), and ResNet-50 (84%). CBAM is proven to improve MobileNetV2 performance with an 11% increase in accuracy. Grad-CAM visualization shows that MobileNetV2-CBAM can better focus on localizing important regions in breast cancer images, providing clearer explanations and assisting medical personnel in diagnosis.
Pelatihan Literasi Numerasi Imersif Bebasis VR untuk Peningkatan Kompetensi Guru di KKG Rayon 1 Waisai Rokhima, Nur; Pamungkas, Dwi; Setiawan, Agus; Nasien, Dewi; Putri, Ramalia Noratama; Tavip, Achmad; Nursalim; Oraple, Ezri Trivena
JDISTIRA - Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Vol. 5 No. 1 (2025)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jdt.v5i1.1110

Abstract

Langkah strategis untuk meningkatkan kualitas pendidikan adalah memberikan pelatihan literasi numerasi, terutama di era globalisasi yang menuntut penguasaan teknologi kontemporer. Tujuan program adalah untuk memberikan 40 guru anggota KKG Rayon 1 Waisai, Raja Ampat, pelatihan literasi numerasi melalui virtual reality (VR). Program ini memberikan pendampingan intensif kepada peserta untuk memastikan keterampilan yang diajarkan dapat diterapkan secara efektif. Metode yang digunakan termasuk memberikan instruksi tentang pengoperasian perangkat VR, membuat konten pembelajaran berbasis numerasi yang menggunakan kearifan lokal Papua, dan menerapkan VR dalam kegiatan pembelajaran di kelas. Hasil program menunjukkan bahwa guru memiliki kemampuan yang lebih baik untuk menggunakan perangkat virtual reality dan membangun skenario pembelajaran numerasi yang berbasis teknologi. Melalui evaluasi pascapelatihan, terjadi peningkatan kompetensi guru dalam pemanfaatan teknologi VR sebesar 85%, berdasarkan hasil angket dan wawancara. Siswa juga menunjukkan peningkatan pemahaman numerasi sebesar 70%, yang diukur melalui hasil tes literasi numerasi sebelum dan sesudah implementasi. Partisipasi aktif peserta dalam pelatihan mencapai 95%, mencerminkan antusiasme yang tinggi terhadap metode pembelajaran berbasis teknologi. Dengan menggabungkan teknologi modern dengan kearifan lokal, program ini menghasilkan pembelajaran yang relevan dan inventif. Dalam waktu dekat, diharapkan pelatihan ini dapat diterapkan di wilayah lain untuk membantu transformasi pendidikan berbasis teknologi di Indonesia.
Analysis of the Among System-Based Discovery and Inquiry Learning Models Hermita, Neni; Putra, Zetra Hainul; Alim, Jesi Alexander; Fitriani, Mike; Nasien, Dewi; Mahbubah, Khoiro
JOURNAL OF TEACHING AND LEARNING IN ELEMENTARY EDUCATION (JTLEE) Vol. 5 No. 2 (2022): August 2022
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33578/

Abstract

This study compares the syntax of the discovery learning and inquiry learning models based on the Among system. This research was a descriptive type of research and used a comparative design. The method of data collection in this research was a literature study. The literature study used is research that is in accordance with the research of researchers and the latest research. The results found that the learning model of discovery learning and inquiry learning based from the Among system was very well implemented in elementary schools. The use of these various models can be adapted to the needs of the educator. Along with the literature study, these two models can support students' abilities and create meaningful learning for students.
Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk Nasien, Dewi; Owen, Steven; Fenly, Fenly; Johanes, Johanes; Lombu, Frendly; Leo, Leo; Baharum, Zirawani
Journal of Applied Business and Technology Vol. 6 No. 3 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i3.201

Abstract

This study aims to optimize the classification of obesity risk at an early stage using Principal Component Analysis (PCA), which is an important technique in machine learning. PCA is used to reduce the dimensionality of data, maintain important information without losing data, and has the advantage of reducing complexity which usually increases the risk of overfitting. The obesity dataset will be classified using algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting Linear, and XGBoost. Specifically, each algorithm is chosen because of its respective advantages: KNN for nonlinear data, SVM for high-dimensional data, and Random Forest and XGBoost for complex data patterns. Evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to assess the performance of the algorithm. The results show that the Random Forest and XGBoost algorithms provide the best performance in terms of accuracy, especially when all dataset features are used without PCA reduction. This study is expected to be a consideration in determining the best algorithm for obesity classification, supporting early detection, and facilitating decision making in health analysis.
Perbandingan Implementasi Machine Learning Menggunakan Metode KNN, Naive Bayes, dan Logistik Regression Untuk Mengklasifikasi Penyakit Diabetes Dewi Nasien; Darwin, Ricalvin; Cia, Alexander; Leo Winata, Andrean; Go, Jerry; M.C, Richard; Charles Wijaya, Ryan; Charles Lo, Kevin
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i1.640

Abstract

Penyakit diabetes menjadi sorotan karena sifatnya yang kronis, dengan gejala utama berupa peningkatan kadar gula darah di atas batas normal. Diabetes terjadi ketika tubuh tidak dapat efisien mengambil glukosa ke dalam sel untuk diubah menjadi energi, menyebabkan penumpukan gula ekstra dalam aliran darah. Penelitian ini menggunakan ekstraksi fitur dengan Analisis Komponen Utama (Principal Component Analysis - PCA) dengan threshold 80%, menghasilkan 5 fitur utama. Fitur-fitur ini kemudian digunakan sebagai input untuk tiga classifier, yaitu K-Nearest Neighbors (KNN), Naive Bayes, dan Regresi Logistik. Data yang digunakan berasal dari Kaggle, dengan pembagian data 70:30 dan 80:20. Hasil penelitian menunjukkan bahwa metode Naive Bayes memberikan akurasi terbaik, mencapai 79% pada pembagian data 80:20. Oleh karena itu, dapat disimpulkan bahwa algoritma Naive Bayes adalah pilihan terbaik untuk klasifikasi data diabetes dalam penelitian ini.
Klasifikasi Penyakit Jantung Menggunakan Decision Tree dan KNN Menggunakan Ektraksi Fitur PCA Dewi Nasien; Sirvan, Sirvan; Deny, Deny; Ryan Syahputra, Ryan Syahputra; Akbar Marunduri, Alberta; Prawinata See, Richardo
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i1.641

Abstract

Penyakit jantung, yang merupakan penyebab utama kematian, menjadi fokus penanganan dan pembiayaan BPJS Kesehatan. Untuk upaya preventif, prediksi penyakit jantung pada pasien menjadi langkah penting. Dalam penelitian ini, proses klasifikasi dilakukan menggunakan dua metode, yaitu decision tree dan KNN, untuk memprediksi penyakit jantung. Metode decision tree dan KNN merupakan pendekatan yang umum digunakan dalam klasifikasi penyakit jantung. Decision tree membangun model keputusan berbasis pohon, sedangkan KNN menggabungkan beberapa decision tree untuk meningkatkan kinerja dan kestabilan prediksi. Hasil evaluasi performa kedua metode dapat memberikan pandangan yang komprehensif tentang keefektifan masing-masing dalam memprediksi penyakit jantung pada dataset yang digunakan. Metrik evaluasi seperti akurasi, precision, recall, dan F1 score akan memberikan informasi tentang sejauh mana model mampu mengklasifikasikan data dengan benar dan mengidentifikasi kasus penyakit jantung dengan baik
PENCARIAN LOKASI FOTOGRAFI ASIA HERTITAGE BERBASIS LOCATION BASED SERVICE (LBS) MENGGUNAKAN ALGORITMA A-STAR Pandapotan, Boris Yosua; Nasien, Dewi
Jurnal Manajamen Informatika Jayakarta Vol 4 No 1 (2024): JMI Jayakarta (February 2024) Seleksi Paper SENATIKA-4 (October 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v4i1.1302

Abstract

This research is conducted by Asia Heritage Pekanbaru, which is one of recreational culinary and tourism in Pekanbaru. Asia Heritage offers comprehensive options of attractions, culinary, and photography spots. Still, it has considerable setbacks being less en-route information and signs and pictures to symbolize the tracks en route to the place. This research is on developing a Location Based Service (LBS) application that utilizes A Star Algorithm. This application can detect object position and generates the shortest effective routes to the designated spot by using A Star Algorithm's active reaction to the entitled class. This research later reveals an application named "Asia Heritage." It is a Photographic application that helps and gives the user the features of available photo-on spots, location, attractions, detailed information, and creative editing for a unique and magnifying experience for each user.
Co-Authors Adiya, M. Hasmil Agus Joko Purwanto Agus Setiawan ahmad kamal, ahmad Ahmad Mulyadi Akbar Marunduri, Alberta Alin Meisya Putri Alyauma Hajjah Amalia Sapriati Andi Andi Anggara, Devi Willeam Angriawan, Sherkhing Anwar Senen Baharum, Zirawani Butar-Butar, Rio Juan Hendri Charles Lo, Kevin Charles Wijaya, Ryan Cia, Alexander Cici Oktaviani Dahliyusmanto, Dahliyusmanto Darwin, Ricalvin Deny Deny, Deny Deny Jollyta Desnelita, Yenny Devi Willieam Anggara Diniya, Diniya Dipuja, Diah Anugrah Erlin Erma Yunita Farkhan, Mochammad Fenly, Fenly Feri Candra Firman Afriadi Fitri Indriani Fitriani, Mike Go, Jerry Gusman, Taufik Gustientiedina Habibollah Haron Ihsan, M. Nurul Iis Afrianty Iis Afrianty Imran B. Mu’azam Jack Billie Chandra Jesi Alexander Alim Johan Johan Johanes Johanes, Johanes Leo Winata, Andrean Leo, Leo Lina Warlina Lombu, Frendly M.C, Richard Mahbubah, Khoiro Mahmud Dwi Sulistiyo Marlim, Yulvia Nora Mestika Sekarwinahyu Mike Fitriani Muhammad Rakha Muhammad Ridha Nazara, Elvin Meiwati Neni Hermita Nopendri Nopendri Nor Fatihah Ismail Nurwijayanti Oraple, Ezri Trivena Owen, Steven Pamungkas, Dwi Pandapotan, Boris Yosua Prawinata See, Richardo Putra Yansen, Eka Rahmadhani, Ummi Sri Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ria Asrina Marza Rianda, Gilang Rio Asikin Rio Rokhima, Nur Roni Sanjaya Ryan Syahputra, Ryan Syahputra Salama A. Mostafa Samah, Azurah A. Sardius, Sardius Setiawan, Laurensius Rendi Siddik, M. Sirait, Andrio Pratama Sirvan, Sirvan Sri Tatminingsih Sukabul, Ahmad Suliana Supriati, Amelia Suroyo Suroyo Tavip, Achmad Wicaksono, Mahfuzan Hadi Wijaya, Tommy Tanu Wilda Susanti Yacob, Azliza Yuli Astuti Yulianti, Deni Yusnita Rahayu Zetra Hainul Putra Zeva Adi Fianto Zirawani Baharum