Garuda - Garba Rujukan Digital

Penerapan UI/UX dengan Metode Design Thinking (Studi Kasus: Warung Makan) Faruq Aziz; Daniati Uki Eka Saputri; Nurul Khasanah; Taopik Hidayat
Jurnal Infortech Vol 5, No 1 (2023): JUNI 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v5i1.15156

Pemesanan makanan di warung tradisional seringkali masih dilakukan secara manual. Hal ini dapat menyebabkan kesalahan dalam pemesanan sehingga dapat menurunkan tingkat kepuasan pelanggan. Penerapan User Interface (UI) dan User Experience (UX) dalam pengembangan produk digital untuk mengatasi masalah tersebut semakin penting dilakukan saat ini. Perlu dilakukan suatu desain yang tepat agar aplikasi tersebut dapat memberikan kemudahan bagi pelanggan dalam melakukan pemesanan, serta memenuhi kebutuhan warung dalam mengelola pesanan. Tujuan dari penelitian ini adalah untuk mengevaluasi dan meningkatkan pengalaman pengguna melalui penerapan metode Design Thinking pada desain aplikasi pemesanan makanan pada warung makan berbasis mobile. Metode System Usability Scale (SUS) dan User Experience Questionnaire (UEQ) digunakan untuk menguji kepuasan pengguna terhadap aplikasi yang dikembangkan dengan menggunakan metode Design Thinking. Hasil dari penelitian ini menunjukkan bahwa aplikasi yang dikembangkan dengan menggabungkan metode Design Thinking dan pengujian menggunakan metode SUS dan UEQ memiliki tingkat kepuasan yang cukup tinggi dari sisi pengguna serta dapat memberikan rekomendasi untuk pengembangan aplikasi pemesanan makanan yang dapat meningkatkan kepuasan pengguna dan meningkatkan daya saing warung.

RANCANG BANGUN CHATBOT UNTUK MENINGKATKAN PELAYANAN CUSTOMER PADA APLIKASI TRAVELOKA YOANDA, YURIKA PRISILIA; Nurmalasari, Nurmalasari; Hidayat, Taopik
Jurnal Teknologi Sistem Informasi Vol 3 No 2 (2022): Jurnal Teknologi Sistem Informasi
Publisher : Program Studi Sistem Informasi, Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jtsi.v3i2.2706

Sebagai Startup besar yang memiliki banyak pengguna dan transaksi setiap harinya, maka perlu adanya peran customer service di dalamnya untuk membantu pengguna yang mengalami kendala atau kesulitan saat melakukan transaksi di aplikasi Traveloka. Peran customer service akan baik bila dapat melayani segala kendala atau kesulitan yang dihadapi pengguna secara langsung. Metode Agile merupakan pendekatan yang iterative dan evalusioner yang dilakukan dengan mengedepankan kolaborasi serta menggunakan dokumen formal yang terbatas dan tepat untuk membangun perangkat lunak yang berkualitas dalam hal biaya yang efektif serta waktu sesuai kebutuhan stakeholder yang bisa berubah-ubah. Aplikasi chatbot ini dibuat dengan menggunakan metode Agile dan framework scrum berbasis Cloud Computing yang artinya semua model machine learning dan API dibuat secara total menggunakan teknologi cloud. Dengan adanya chatbot aplikasi Traveloka dapat meningkatkan pelayanan terhadap customer secara maksimal dan juga mempermudah customer untuk mencari informasi dan solusi dalam mengatasi masalah yang dihadapi. Kata Kunci : Chatbot, cloud, API ,Model, Agile

Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model Daniati Uki Eka Saputri; Nurul Khasanah; Aziz, Faruq; Taopik Hidayat
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.14

Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.

APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT Saputri, Daniati Uki Eka; Aziz, Faruq; Khasanah, Nurul; Hidayat, Taopik; Septian, Rendi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5532

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.

ANALISIS PERCEPATAN PEMULIHAN EKONOMI INDONESIA PASCA PANDEMI DENGAN BIG DATA DAN DEEP LEARNING Rina, Rina; Hidayat, Taopik; Uki Eka Saputri, Daniati
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 3 (2024): JATI Vol. 8 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i3.9420

Penelitian ini membahas tentang percepatan pemulihan ekonomi Indonesia pasca pandemi COVID-19 melalui pendekatan analisis big data dengan penerapan teknik machine learning dan deep learning. Dengan munculnya pandemi pada akhir 2019, dampaknya menyebar ke seluruh dunia dan memicu serangkaian kebijakan, termasuk Pembatasan Sosial Berskala Besar (PSBB) di Indonesia. Kebijakan ini, sementara membantu menanggulangi penyebaran virus, namun secara signifikan menghentikan aktivitas ekonomi, terutama di sektor usaha kecil dan menengah (UMKM). Studi sebelumnya menyoroti dampak ekonomi global, kinerja keuangan di bursa efek Indonesia, dan bahkan implikasinya terhadap UMKM. Penelitian ini bertujuan mengkaji upaya percepatan pemulihan ekonomi di Indonesia pasca pandemi dengan memanfaatkan data teks dari berita dan ulasan pengguna hotel. Dengan menggunakan algoritma machine learning seperti Naive Bayes, Support Vector Machine, dan Random Forest, serta algoritma deep learning seperti Attention Mechanism dan Bidirectional LSTM, penelitian ini berusaha menghasilkan wawasan mendalam tentang tren dan pola perilaku masyarakat pasca pandemi. Data diperoleh dari sumber publik online dan diharapkan dapat memberikan kontribusi bagi pemangku kepentingan dalam merancang strategi pemulihan ekonomi yang efektif. Melalui analisis data teks yang komprehensif, penelitian ini diharapkan dapat memberikan pandangan tren dan pola perilaku dari data yang besar untuk memahami dinamika pemulihan ekonomi Indonesia dan memandu kebijakan yang lebih tepat sasaran. Hasil penelitian ini memperlihatkan tingkat akurasi masing-masing algoritma: SVM 88%, MultinomialNB 78%, Random Forest 84%, dan Bidirectional LSTM 92%. Analisis dilakukan pada data ulasan pengguna Traveloka, menunjukkan lonjakan signifikan dalam ulasan aplikasi. Hal ini mengindikasikan peningkatan penggunaan aplikasi, mencerminkan masyarakat yang mulai berpergian pasca pandemi, berpotensi mempengaruhi ekonomi Indonesia.

Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur Khasanah, Nurul; Eka Saputri , Daniati Uki; Aziz, Faruq; Hidayat, Taopik
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.5522

Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.

Blockchain's Impact on Coffee Supply Chains: A Systematic Literature Review Rina, Rina; Rahmayu, Mulia; Hidayat, Taopik
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 11, No 1 (2025): Periode Januari 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v11i1.24416

Coffee is a globally significant commodity with high economic value and extensive social impact. Nevertheless, the intricate nature of the coffee supply chain frequently leads to issues concerning visibility, traceability, and sustainability. Blockchain offers a decentralized, transparent, and secure approach to mitigate these problems effectively. This research seeks to explore the current trends, core motivations, and auxiliary technologies that facilitate the integration of blockchain technology within the coffee supply chain. A total of 16 scientific articles published in Scopus-indexed journals (Q1 to Q4) between 2019 and 2024 were selected and analyzed using the Systematic Literature Review (SLR) method. The findings reveal that traceability and transparency are the primary focuses of blockchain implementation. The study also identifies several supporting technologies, such as smart contracts, Distributed Ledger Technology, IoT, NFT, and Artificial Intelligence, which play critical roles in enhancing the efficiency and security of blockchain-based coffee supply chains.

Multiclass Meat Classification Using a Hybrid Machine Learning Approach Taopik Hidayat; Daniati Uki Eka Saputri; Faruq Aziz; Nurul Khasanah
International Journal of Computer Technology and Science Vol. 2 No. 2 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i2.238

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.

Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur Khasanah, Nurul; Eka Saputri , Daniati Uki; Aziz, Faruq; Hidayat, Taopik
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.5522

Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.

Prediksi Kelulusan Mahasiswa Menggunakan Algoritma C4.5 dengan RapidMiner: Studi Kasus Data Akademik Perguruan Tinggi XYZ Khasanah, Nurul; Saputri, Daniati Uki Eka; Hidayat, Taopik; Aziz, Faruq
Indonesian Journal Computer Science Vol. 4 No. 2 (2025): Oktober 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v4i2.9647

Ketepatan waktu kelulusan mahasiswa merupakan indikator penting dalam menilai kualitas dan efektivitas pendidikan tinggi. Keterlambatan kelulusan tidak hanya berdampak pada akreditasi program studi, tetapi juga pada efisiensi manajemen akademik dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini bertujuan membangun model prediksi kelulusan mahasiswa menggunakan algoritma C4.5 berbasis pohon keputusan dengan dukungan perangkat lunak RapidMiner. Dataset yang digunakan terdiri atas 379 entri mahasiswa, yang mencakup atribut demografis (jenis kelamin, umur, status nikah), status mahasiswa, indeks prestasi semester (IPS 1–8), serta indeks prestasi kumulatif (IPK). Proses penelitian meliputi pengumpulan, pembersihan, transformasi data, pemodelan, dan evaluasi performa. Model diuji menggunakan pembagian data 70:30 serta validasi silang (10-fold cross-validation) untuk memastikan keandalan hasil. Hasil pengujian menunjukkan akurasi 97,81% dan nilai AUC 0,991, yang menegaskan kemampuan algoritma C4.5 dalam mengklasifikasikan status kelulusan secara tepat. Temuan ini menunjukkan peningkatan signifikan dibandingkan penelitian sebelumnya dengan algoritma Naïve Bayes (88,16%) dan K-NN (87,8%). Atribut yang paling berpengaruh adalah IPS3, IPS4, IPS5, status pekerjaan, dan umur mahasiswa. Penelitian ini berkontribusi pada pengembangan model prediksi yang tidak hanya akurat, tetapi juga mudah diinterpretasikan, sehingga dapat dimanfaatkan oleh institusi pendidikan dalam menyusun kebijakan intervensi akademik dini bagi mahasiswa berisiko terlambat lulus.

Title

Found 26 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 26 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 26 Documents
Search