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All Journal Jurnal Teknik Informatika Jurnal Informatika dan Teknik Elektro Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia Informatik : Jurnal Ilmu Komputer Kwangsan: Jurnal Teknologi Pendidikan Informatika Community Empowerment ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat Patikala Indonesian Community Journal Jurnal Teknologi Informasi Cyberku Jurnal Sains dan Teknologi ISTP SAGA: Journal of Technology and Information Systems "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Kreativitas Pada Pengabdian Masyarakat (Krepa) Nuansa Informatika Kohesi: Jurnal Sains dan Teknologi Abdi Akommedia : Jurnal Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Teknik Elektro dan Komputer Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology Riau Jurnal Teknik Informatika Jurnal Ilmiah Informatika dan Komputer Applied Information Technology and Computer Science (AICOMS) ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia Advance Sustainable Science, Engineering and Technology (ASSET) Jejak digital: Jurnal Ilmiah Multidisiplin Edu Komputika Journal Variable Research Journal Jurnal Ilmiah Literasi Indonesia Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat INTECH - Informatika Dan Teknologi
Ade Yusupa
Universitas Sam Ratulangi, Manado, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Design Thinking-Based UI/UX for a Clean Water Education App: Perancangan UI/UX Aplikasi Edukasi Air Bersih Berbasis Metode Design Thinking Adriano Putra Imanuel Posumah; Rangian Elroi Johanes; Victor Obetnego Johanes Senduk; Yosua Jackvin Kumontoy; Virginia Tulenan; Ade Yusupa
Jurnal Teknik Elektro dan Komputer Vol. 15 No. 1 (2026): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v15i1.65175

Abstract

Abstract — Clean water is a fundamental necessity for human health and survival, yet public awareness of the importance of sustainable water resource management remains relatively low. Limited understanding of environmental pollution and efficient daily water usage presents a major challenge that requires effective educational solutions. This study aims to design UI/UX of a mobile-based clean water education application as an interactive learning medium to enhance public awareness. A comprehensive Design Thinking approach comprising the empathize, define, ideate, prototype, and test stages was employed to ensure that the proposed design is user-centered. A high-fidelity prototype was developed using Figma and evaluated by 20 participants through task-based usability testing and the System Usability Scale (SUS) questionnaire. The evaluation results indicate that the application achieved an average SUS score of 81.25, placing it in the Acceptable category and corresponding to an acceptance grade of A. These findings demonstrate that a user-centered design approach is effective in creating an intuitive and easy-to-use interface, with strong potential to improve public literacy regarding clean water management. Key words— Clean Water; Design Thinking; Digital Education; System Usability Scale; UI/UX   Abstrak —Air bersih merupakan kebutuhan fundamental bagi keberlangsungan hidup dan kesehatan manusia, namun tingkat kesadaran masyarakat terhadap urgensi pengelolaan sumber daya air yang berkelanjutan masih tergolong rendah. Kurangnya pemahaman mengenai dampak pencemaran lingkungan dan efisiensi penggunaan air sehari-hari menjadi tantangan utama yang memerlukan solusi edukatif. Penelitian ini bertujuan untuk merancang UI/UX aplikasi edukasi air bersih berbasis mobile sebagai media pembelajaran interaktif guna meningkatkan kesadaran masyarakat. Penelitian ini menerapkan metode Design Thinking secara komprehensif yang meliputi tahapan empathize, define, ideate, prototype, dan test untuk memastikan solusi desain berpusat pada kebutuhan pengguna. Prototipe high-fidelity dikembangkan menggunakan perangkat lunak Figma dan dievaluasi oleh 20 partisipan melalui pengujian kegunaan berbasis tugas serta kuesioner System Usability Scale (SUS). Hasil pengujian menunjukkan bahwa aplikasi memperoleh skor SUS rata-rata sebesar 81,25, yang menempatkannya dalam kategori Acceptable dengan skala penerimaan Grade A. Temuan ini membuktikan bahwa pendekatan desain berbasis pengguna efektif menciptakan antarmuka yang intuitif, mudah digunakan, serta berpotensi kuat dalam meningkatkan literasi masyarakat mengenai pengelolaan air bersih. Kata kunci — Air Bersih; Design Thinking; Edukasi Digital; System Usability Scale; UI/UX
Analisis Penggunaan Warna Berdasarkan Teori Warna Menurut Brewster Pada UI Aplikasi E-wallet DANA Menggunakan Pendekatan Design Thinking Flouresita Udampo; Stevina Lembong; Ade Yusupa; Victor Tarigan
INTECH Vol. 6 No. 2 (2025): INTECH (Informatika Dan Teknologi)
Publisher : Informatics Study Program, Faculty of Engineering and Computers, Baturaja University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54895/intech.v6i2.3055

Abstract

Penelitian ini mengkaji penggunaan Teori Warna Brewster pada antarmuka (UI) aplikasi e?wallet DANA untuk meningkatkan keterbacaan, rasa aman, dan kepercayaan pengguna. Fokus penelitian meliputi analisis kontras warna, skema penyesuaian tema, serta evaluasi kegunaan melalui tahapan Design Thinking: Empathize, Define, Ideate, Prototype, dan Testing. Data dikumpulkan dari 21 responden usia 18–35 tahun menggunakan kuesioner System Usability Scale (SUS) dan metrik HEART, serta diuji pada prototipe low? dan high?fidelity. Hasil menunjukkan skor rata?rata SUS 72,41 (kategori B) dan HEART 69,6%, mengonfirmasi peningkatan kenyamanan, keterlibatan, dan retensi. Simpulan menegaskan bahwa pemilihan warna yang tepat memperkuat identitas merek DANA dan pengalaman pengguna, dengan rekomendasi fitur personalisasi warna dan pratinjau tema.
Penerapan Collaborative Filtering untuk Sistem Rekomendasi Film Rachel Pangemanan; Nasya Emanuel Soekamto; Glerio Adrian; Ade Yusupa; Victor Tarigan
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3248

Abstract

Sistem rekomendasi berperan penting dalam membantu pengguna menemukan konten yang relevan di tengah banyaknya informasi yang tersedia. Penelitian ini mengimplementasikan dan mengevaluasi metode User-Based dan Item-Based Collaborative Filtering untuk sistem rekomendasi film menggunakan dataset MovieLens 100K. Evaluasi dilakukan menggunakan RMSE, MAE, Precision, Recall, dan F1-Score untuk mengukur akurasi prediksi dan relevansi rekomendasi. Hasil penelitian menunjukkan bahwa metode Item-Based Collaborative Filtering memiliki performa lebih baik dibandingkan User-Based Collaborative Filtering dalam hal akurasi prediksi dan relevansi rekomendasi. Keunggulan ini disebabkan oleh stabilitas hubungan antar item dibandingkan preferensi pengguna yang lebih dinamis. Meskipun efektif, metode ini masih menghadapi tantangan seperti sparsity dan keterbatasan jumlah rating pada beberapa film. Penelitian selanjutnya dapat mengeksplorasi pendekatan hibrida yang menggabungkan Collaborative Filtering dengan deep learning atau content-based filtering untuk meningkatkan kualitas
Perbandingan Algoritma Naive Bayes Dan Support Vector Machine Dalam Deteksi Berita Hoax Berbahasa Indonesia Keren Mumbunan; Michyta Marchantia Betsi Bawata; Miracle Prayer kusen; Victor Tarigan; Ade Yusupa
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3262

Abstract

Abstrak Berita hoax menjadi masalah besar di era digital, terutama di Indonesia, di mana informasi yang tidak terverifikasi menyebar dengan cepat melalui media sosial. Penelitian ini membandingkan kinerja algoritma Naïve Bayes (NB) dan Support Vector Machine (SVM) dalam mendeteksi berita hoax berbahasa Indonesia. Dataset yang digunakan terdiri dari 4.599 berita, yang dikumpulkan dari Twitter dan repositori GitHub, dikategorikan sebagai hoax atau valid. Berbagai tahap preprocessing, seperti tokenisasi, stopword removal, stemming, dan TF-IDF vectorization, diterapkan untuk meningkatkan akurasi model. Model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa SVM memiliki performa lebih baik dibandingkan Naïve Bayes, dengan akurasi 70,87%, lebih tinggi dibandingkan 66,52% dari Naïve Bayes. SVM juga unggul dalam presisi (72%) dan F1-score (82%), sedangkan Naïve Bayes lebih unggul dalam recall (99%). Kesimpulan dari penelitian ini adalah bahwa SVM lebih efektif dalam klasifikasi berita hoax, sementara Naïve Bayes lebih cocok digunakan jika kecepatan pelatihan menjadi prioritas. Penelitian selanjutnya disarankan untuk menggunakan pendekatan deep learning seperti BERT atau LSTM, memperluas dataset, serta mengembangkan model hybrid yang menggabungkan Naïve Bayes dan SVM untuk mengoptimalkan akurasi dan efisiensi.
Apriori and FP-Growth Comparative Analysis of MPL Indonesia Season 13 Hero Drafts Andre Immanuel Porayou; Jacob Alfanicolls Rahayaan; Ade Yusupa; Yaulie Deo Y. Rindengan
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): June 2026
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.524

Abstract

Hero combination selection or drafting is a crucial factor in determining victory in Mobile Legends: Bang Bang (MLBB) games at the professional level such as MPL Indonesia Season 13. However, counter-pick strategies are often based solely on the subjective intuition of players or coaches. This study aims to provide an objective basis for determining winning hero combination patterns by applying the Association Rule Mining (ARM) technique. Two main algorithms, namely Apriori and Frequent Pattern Growth (FP-Growth), are compared to evaluate the performance efficiency and accuracy of the resulting rules. The research data includes 183 winning transactions during the regular season of MPL ID Season 13, with parameters of minimum support 0.05 (5%), minimum confidence 0.40 (40%), and minimum lift 1.2. The results show that the strongest association rules are found in the combinations {Lapu-lapu} → {Fredrinn} (confidence 0.71) and {Cici} → {Fredrinn} (confidence 0.59). In terms of technical performance, the Apriori algorithm recorded a faster execution time than FP-Growth on this dataset. This study concluded that both algorithms produce identical association rule outputs, while Apriori demonstrated faster execution on this small-scale dataset, a finding attributed to the limited transaction volume rather than a universal superiority of Apriori over FP-Growth. The resulting rules can serve as a data-driven strategic recommendation system for professional esports teams in the pick and ban phase.
Comparative Analysis of Computer Vision Models for Detecting Nilam Plant Diseases: A Case Study of MobileNet vs. YOLO Jimmy Robot; Nancy Tuturoong; Ade Yusupa
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12800

Abstract

Nilam Plant (Patchouli plant) located in Minahasa Regency can be affected by several diseases that can significantly reduce their essential oil capacity. The common practice of monitoring crops for diagnosis relies on labor-intensive methods that can be variable in accuracy, depending on previous experience of the tended. The main goal of the study is to develop and to compare model performance using Deep Learning computer vision in monitoring conditions of patchouli plants with respect to different conditions (healthy, bacterial wilt, viral, and budok). This study assess and compare MobileNet (a lightweight classifier) against Deep Learning based object detectors (YOLOv5, v8, v11) using an underlying dataset that was created unimpeachably in a natural patchouli field setting, consisting of 3,000 images which contain 3,820 annotated bounding boxes, across 4 classes (Healthy, Bacterial Wilt, Viral, Budok). Evaluation reveals a clear trade-off between the two models. MobileNet finds (nearly perfect) classification accuracy of 94.7% (F1-score>0.90 for all classes), while the faster YOLOv8l yields an 88.2% mAP50. Both models had the hardest time dealing with the "Viral" class due to visual similarities it shared with the healthy class (F1: 0.90, mAP: 0.81). MobileNet produced better accuracy (94.7%) but had slower inference time (3.0s). YOLOv8l provided real-time detection (1.4s) but lower mAP (88.2%). Our recommendation is a hybrid 2-stage system (YOLO-drone scan; MobileNet-farmer confirmation) as an operational approach for Precision Agriculture in Patchouli farming. Overall, the main takeaway is that: MobileNet is intended for diagnostic application and YOLOv8 superior for real-time video-based field monitoring.
Evaluating Disruption Risk to Foster Resilience in Humanitarian Supply Chain:  An Integrated SV-PSI and EDAS Model Agung Sutrisno; Christian Spreafico; Muhammad Dwisnanto Putro; Ade Yusupa; Amir Tjolleng; Kenji Lokaputra
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3345

Abstract

The evaluation of the risks in humanitarian supply chain operations is crucial to prevent losses due to disasters. However, many quantitative studies in the literature are aimed at the profit-oriented supply chain, while those in the field are based on the subjective preferences of decision-makers, making risk prioritization less accurate. To fill this gap, this study presents a theoretically improved approach for this purpose by integrating statistical variance, preference selection index (PSI), and EDAS methods into humanitarian supply chain FMEA to prioritize risks. To demonstrate the applicability of the model, numerical calculations and sensitivity tests were conducted using secondary data. The test results indicate that the dimension of risk detection capability is an important dimension for proactive risk prevention. Furthermore, communication in humanitarian disaster response supply chain operations is a very important enabler for the resilience of the humanitarian supply chain. In the future, inclusion of the influence of relationships between decision criteria and between risk factors is recommended as a direction for further research from this study.
Co-Authors Abdon E. Sayori Abdul Syukur Adabaye, Sarmila Adinda Franky Nelwan Aditya Lapu Kalua Adriano Putra Imanuel Posumah Agatha Biring Agung Sutrisno Akay, Yuri Amir Tjolleng Andika Pratama Putra Nugraha Andre Immanuel Porayou Anggreini Prisilia Lumi Bakti, Andi Ikhtiar Barca Sembeng Batjo, Lourdes BATJO, LOURDES CHRISTIAN PASKAH Benedict, Edward Bernad Jumadi Dehotman Sitompul Beryl Wakas Beverly Daniel Velix Rauan Biring, Agatha Biringpasemba, Thya Oksabella Blessy Tefilla Tangkuman Brave A. Sugiarso Christian Spreafico Cleantha Polii Daniel Febrian Sengkey Dhiva Runkat Dringhuzen J. Mamahit Dringhuzen Mamahit Dringhuzen Mamahit, Dringhuzen Eman, Jonathan Emmanuela Sembeng Erlica M.I. Tutu Fabiano Antonius Mamangkey, Xavier Fajar Salinding Buntu Payuk Farsit Manoppo, Faldy Febiola Lengkong Fithratul Zalmi, Wahyuni Flouresita Udampo Genggong, Arkafali PF Geoffrey G Matantu Gigir, Winston Gizelda Felicya Jeanette Lewu Glerio Adrian Happyeaster Nathanael Maindoka Heavenly E. Kawatu Herni Herawati Elisabeth Abraham Hosiana S. R. Wuwungan Illahi, Nabil Irvan Trang Jacob Alfanicolls Rahayaan Jeheskiel Liwe Jeremia Siregar Jeremia, Imanuel Jimmy Reagen Robot Jimmy Robot Jontinus Manullang Joseph Alexander Wowor Jourgent Ligouw Kalambia, Audia Endondaya Kaligis, Jonathan Kaunang, Ronaldino Kendek Allo, Yusi Meilany Kenji Lokaputra Kenneth Palilingan Keren Mumbunan Kerenhapukh Priskila Waworuntu Kevin, Darmansyah Geraldy Kezia Aurelya Mbatono Kolang, Indri Claudia kurnia, rahmi putri Las D. Hutasoit Lasut, Aleeisandro Leatemia, Kevin Legi, Kevin Lelemboto, Jeremia Lie, Nobiana Louisa Lolong Luis Manoppo M. Ilham Akbar HS, A. Made Hendy Wijaya, I Mailangkay, Roynaldo Makasunggal, Juan Natanel mamoto, Anugerah Manoppo, Jennifer Manoppo, Michael Reynald Mantik, Joy Timoty Markus Karamoy Umboh Martien Mengko, Riky Mawara, Reza Michelly Cantika Michyta Marchantia Betsi Bawata Miracle Prayer kusen Moh. Fachruddin Suharto Mokansi, Misael MOKANSI, MISAEL TELDI Moniaga, Praise Muhammad Dwisnanto Putro Mundung, Theogravo Fidelino Nadira Tri Ardianti Purnomo Nancy Jeane Tuturoong Nancy Tuturoong Nasib Marbun Nasya Emanuel Soekamto Natalia Kristiani Pangemanan Natasha Pangemanan Nelwan, Christa Kitsy Ngama, Kezia Niken Febiana Putri Pakaya Niken Pakaya Nur Fiat, Daffa Nur Nurul Rizkyani Paat, Margareth Paath, Rivaldo Paendong, Indah Pangemanan, Jonatan Pangemanan, Natasha Parihala, Lordy Lawrence Pasanda, Marlin Syellen Patanduk, Arpen Payung, Grace Tandi Petiunaung, Joshua Ezra Porayou, Andre Princenoel Wanei Punusingon, Paskal Putung, Frico Rama Rachel Pangemanan Rafby Saputra Mokodompit Rahayaan, Jacob RAHAYAAN, JACOB ALFANICOLLS Rangian Elroi Johanes Reinal Eljre Moningka Reinhard Komansilan Rengkung, Vancel Rewur, Afny Rahel Prastika Ricardus Anggi Pramunendar Rifky P. R. Umar Rombeallo, Gabryela Sagai, Kenzu G. Salassa, Norris Elden Salsabilla Putrihanda Sancia Alicia Taligangsing Sanriomi Sintaro Sary Diane Ekawati Paturusi Sembeng, Barca Sembeng, Emmanuela Shakila Maisa Ayu Sibarani, Alu Sitanggang, Jonathan Sitompul, Natanael Stevina Lembong Sugiarso, Brave A Sugiarso, Brave Angkasa sumarauw, Joanna Sumarno, Anggraini Dwi Putri Sumendap, Rivaldy Immanuel Supoyo, Bertrand Suranta Bill Fatric Ginting Tafuama, Daniel Takalamingan, Gerald Taringan, Victor Tika Putri Agustina Tondang, Andrew Trifena Sweetly Kasenda Turangan, Yermia Umboh, Markus Umboh, Markus Karamoy Victor Obetnego Johanes Senduk Victor Tarigan Victor Taringan Virginia Tulenan Wanda Angella Pantouw Wantasen, Dafa Khairu Fadillah Winston Gigir Wiwik Akhirul Aeni Wungow, Marsel Yaulie Deo Y. Rindengan Yaulie Deo Y. Rindengan Yefta Yosia Asyel Yeremia Euodia Gumolili Yngwie R. V. S. Lumendek Yosua Jackvin Kumontoy Yuri Akay Yuri Vanli Akay Zacharias, Glorya Zefanya Kambey Zuldesmi