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All Journal International Journal of Electrical and Computer Engineering Indonesia Medicus Veterinus TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Elemen Indonesian Journal of Electrical Engineering and Computer Science JagoMIPA: Jurnal Pendidikan Matematika dan IPA Dharmas Education Journal (DE_Journal) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Akbar, Saiful
Data And Software Engineering Research Group School Of Electrical Engineering And Informatics, Institut Teknologi Bandung
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Veterinary Other

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Pengembangan Permainan Math Bingo sebagai Media Evaluasi Capaian Pembelajaran di STKIP PGRI Situbondo Ambarsari, Ida Fitriana; Astindari, Tri; Hasanah, Nur; Akbar, Saiful; Romlah, Romlah
JagoMIPA: Jurnal Pendidikan Matematika dan IPA Vol. 5 No. 2 (2025): JagoMIPA: Jurnal Pendidikan Matematika dan IPA
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/jagomipa.v5i2.1463

Abstract

Penelitian ini dilaksanakan untuk mendeskripsikan pengembangan permainan Math Bingo sebagai media evaluasi capaian pembelajaran pada mata kuliah algoritma dan pemrograman. Penelitian ini dilaksanakan di STKIP PGRI Situbondo pada mahasiswa semester 2 program studi pendidikan matematika dengan 15 mahasiswa sebagai subjek penelitian. Jenis penelitian adalah penelitian kualitatif. Pada penelitian ini dideskripsikan pengembangan dan penerapan permainan Math Bingo sebagai upaya untuk mendeskripsikan teknik penilaian yang lain, selain tes tulis dan proyek. Lebih lanjut, pengumpulan data dilakukan untuk menganalisis respon mahasiswa terhadap pengembangan permainan dan hasil evaluasi capaian pembelajaran. Analisis evaluasi capaian pembelajaran di kelas dilakukan pada saat permainan dilaksanakan serta pada pengisian jawaban dari soal menggunakan Google Form. Data dikumpulkan menggunakan observasi dan kuesioner. Analisis data menggunakan teknik kualitatif. Berdasarkan hasil pengisian kuesioner dan hasil evaluasi capaian pembelajaran diketahui bahwa pelaksanaan pengembangan penerapan permainan Math Bingo, pelaksanaan permainan Math Bingo, mahasiswa yang memberikan respon baik, dan mahasiswa yang berhasil mencapai capaian pembelajaran yang dituju berturut-turut mencapai 95%, 100%, 70%, dan 80%. Jadi, penerapan pengembangan permainan Math Bingo dapat menjadi salah satu media yang efektif untuk mengevaluasi capaian pembelajaran, khususnya pada penelitian ini di STKIP PGRI Situbondo.
Storychart: A Character Interaction Chart for Visualizing the Activities Flow Abidin, Zainal; Munir, Rinaldi; Akbar, Saiful; Mandala, Rila; Widyantoro, Dwi H.
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1608

Abstract

Event-predicate-based storyline extraction results in a chronologically ordered activity journal. The extraction results contain complex human activities, so the activity journal requires a visualization model to describe actor interactions. This paper proposes a chart to visualize the activities' flow to describe the characters' interactions in an activity journal. This chart is called a storychart. Storycharts have an actor channel that can accept single entities or teams. The actor channel allows changing the type from single to a team or vice versa and moving members to other teams. The activity channel serves as a connector to accommodate interactions between actors. The activity channel provides a visual space for the elements of what, where, and when. Event predicates are the core of what. Therefore, the storychart visualizes the event predicate using glyphs to attract the reader’s attention. The main contribution of this paper is to introduce a team channel that can visualize the identity of team members and an activity channel that can visualize the details of events. We invited participants to discover the reader’s perception of the ease of team recognition and the integrity of the meaning of the narrative visualized by the storychart. Participants involved in the evaluation were filtered by literacy score. Evaluation of storychart reading showed that readers could easily distinguish teams from single actors, and storycharts could convey the story in the activity journal with little reduction in meaning.
Prediksi Arus Lalu Lintas Kendaraan Menggunakan Framework Spatio-Temporal Graph Neural Networks Hafizhan, Naufal Dzaki; Akbar, Saiful
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Sistem transportasi cerdas dapat diterapkan dengan salah satu bagiannya yaitu model prediksi lalu lintas untuk mengoptimalkan arus kendaraan sehingga dapat mengurangi kemacetan. Di sisi lain, telah banyak penelitian-penelitian sebelumnya yang berfokus pada pembangunan model prediksi lalu lintas menggunakan metode gabungan yang mempelajari fitur spasial dan temporal secara serentak melalui pendekatan model berbasis graf sedangkan penelitian di Indonesia masih menggunakan metode pembelajaran mesin sederhana. Oleh karena itu, dibangun model prediksi arus lalu lintas dengan dataset yang telah diimputasi oleh model prediktif Graph Imputation Neural Network (GINN) menggunakan framework Spatio-Temporal Graph Neural Network (STGNN). Model yang diusulkan terdiri dari dilated spatio-temporal synchronous graph convolutional module dan dilated temporal convolutional networks untuk menangkap dinamika spasial-temporal baik secara global dan lokal maupun jangka pendek dan jangka panjang, serta dynamic graph learning untuk mempelajari karakteristik dinamis spatial-temporal secara lokal. Graf dinamis yang dihasilkan dapat menyesuaikan pola data lalu lintas dengan baik serta meningkatkan kinerja hasil prediksi. Eksperimen yang dilakukan pada tiga dataset yang telah dibangun dan satu real-world dataset membuktikan bahwa model yang diusulkan dapat melampaui kinerja model state-of-the-art dan metode imputasi yang menggunakan model prediktif mampu meningkatkan kinerja model.   Abstract Intelligent transportation systems can be implemented with one of their components, namely traffic prediction models, to optimize vehicle flow and reduce congestion. On the other hand, many previous studies have focused on developing traffic prediction models using a combined method that simultaneously studies spatial and temporal features through a graph-based model approach, while research in Indonesia still uses simple machine learning methods. Therefore, a traffic flow prediction model was developed using a dataset that had been imputed by Graph Imputation Neural Network (GINN) predictive model using Spatio-Temporal Graph Neural Network (STGNN) framework. The proposed model consists of a dilated spatio-temporal synchronous graph convolutional module and dilated temporal convolutional networks to capture spatio-temporal dynamics both globally and locally as well as in the short and long term, as well as dynamic graph learning to learn local spatio-temporal dynamic characteristics. The resulting dynamic graph can adapt traffic data patterns well and improve prediction performance. Experiments conducted on three constructed datasets and one real-world dataset prove that the proposed model can surpass the performance of state-of-the-art models and that imputation methods using predictive models can improve model performance.
Co-Authors Ahmada, Marsa Amal, Irfan Ambarsari, Ida Fitriana Arifiansyah, Fitra Arlette Suzy Puspa Pertiwi Astindari, Tri Benhard Sitohang Dwi H. Widyantoro Efendi, Dodik Fitra Arifiansyah Hafizhan, Naufal Dzaki Hery Heryanto Ida Bagus Kade Suardana Ida Bagus Komang Ardana Inne Gartina Husein Jasman Pardede Kurniandha Sukma Yunastrian Muhammad Hasan Basri Noer Fadilah, Rina Putri Nur Hasanah Prameswari, Anindya Rasmi Rikmasari Rila Mandala Rinaldi Munir Romlah Suhono Harso Supangkat Susetyo Bagas Bhaskoro Susetyo Bagas Bhaskoro Yunastrian, Kurniandha Yunastrian, Kurniandha Sukma Zainal Abidin