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All Journal Jurnal Rekayasa Hijau MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal Social Science And Technology For Community Service Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Informatika dan Rekayasa Perangkat Lunak Teknologi : Jurnal Ilmiah Sistem Informasi
Putra, Kurnia Ramadhan
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Environmental Science Languange, Linguistic, Communication & Media

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Journal : MIND (Multimedia Artificial Intelligent Networking Database) Journal

 1 
Pemanfaatan Metode Collaborative Filtering dengan Algoritma KNN pada Sistem Rekomendasi Produk PUTRA, KURNIA RAMADHAN; RAHMAN, ILHAM FATHUR
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 1 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i1.113-123

Abstract

AbstrakSalah satu permasalahan customer pada e-commerce adalah sulitnya menemukan produk yang diinginkan untuk dibeli. Sistem rekomendasi mampu menangani permasalahan tersebut dengan cara mengalisis data profil customer untuk menyaring produk yang sesuai dengan profil customer kemudian merekomendasikannya kepada customer tersebut. Untuk mengetahui hubungan antara produk dengan pengguna maka dapat memanfaatkan sistem rekomendasi. Ada beberapa permasalahan pada sistem rekomendasi yaitu sparsity data, missing value, dan duplikasi data yang sering ditemukan pada data berbasis rating seperti pada e-commerce. Untuk menyelesaikan masalah ini, maka diusulkan metode Item-based Collaborative Filtering dan algoritma K-Nearest Neighbor (KNN) dengan hasil evaluasi nilai MAE sebesar 1,05 dan RMSE sebesar 1,36 yang mampu menangani sistem rekomendasi dengan baik dengan tingkat kesalahan yang kecil.Kata kunci: recommendation system, item-based collaborative filtering, KNN, Sparsity Data, Cold-Start.AbstractIn e-commerce, one common customer problem is difficulty in finding the product they want to buy. This issue can be addressed through a recommendation system, which analyzes customer profile data to filter products that match the customer's profile and then recommends them. One way to establish the relationship between products and users is by using a recommendation system. However, recommendation systems often encounter problems such as data sparsity, missing values, and data duplication, particularly in rating-based data. To address these issues, the Item-based Collaborative Filtering method and the K-Nearest Neighbor (KNN) algorithm are proposed. Evaluation results show that these methods have MAE values of 1.05 and RMSE of 1.36, indicating their effectiveness in handling the recommendation system with a low error rate.Keywords: recommendation system, collaborative filtering, item-based CF, KNN
Influence of Data Scaling and Train/Test Split Ratios on LightGBM Efficacy for Obesity Rate Prediction FAHRUDIN, NUR FITRIANTI; PUTRA, KURNIA RAMADHAN; UMAROH, SOFIA; LAUTAN, GAMAS BLOORY
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.220-234

Abstract

AbstrakNormalisasi adalah proses yang tidak dapat dilewatkan dalam data mining yang membantu menyesuaikan nilai atribut data ke skala yang sama. Dalam konteks data mining, perbedaan skala antar atribut dapat menyebabkan kesalahan dalam pemodelan atau interpretasi hasil. Penggunaan normalisasi dalam pra-pemrosesan masih diperdebatkan, terutama ketika menggunakan algoritma dari kelompok pohon keputusan.  Penelitian ini membandingkan model dengan data yang dinormalisasi dan tidak dinormalisasi dengan menggunakan metode normalisasi, MinMaxScaler, MaxAbsScaler, dan RobustScaler. Hasil penelitian menunjukkan bahwa model LightGBM tanpa normalisasi memiliki tingkat akurasi sebesar 96,6 dalam mengklasifikasikan tingkat obesitas pada data saat ini. Tidak hanya normalisasi yang mempengaruhi hasil klasifikasi, tetapi juga jumlah rasio antara data pelatihan dan pengujian. Penelitian menunjukkan bahwa semakin besar persentase data yang digunakan untuk pelatihan, semakin tinggi tingkat akurasinya. Pada dataset obesitas, rasio 80:20 memiliki akurasi hingga 97%.Kata kunci: Decision Tree, LightGBM, Obesitas, Data Mining, KlasifikasiAbstractNormalization is an essential process in data mining that helps adjust the values of data attributes to the same scale. In data mining, differences in attribute scales can lead to errors in modeling or interpreting results. Normalization in preprocessing is still debated, particularly when using algorithms from the decision tree family. This study compares models with normalized and non-normalized data using normalization methods such as MinMaxScaler, MaxAbsScaler, and RobustScaler. The results show that the LightGBM model without normalization achieved an accuracy rate of 96.6% in classifying obesity levels in the current dataset. Not only does normalization affect classification results, but the ratio between training and testing data also plays a role. The study indicates that the larger the percentage of data used for training, the higher the accuracy rate. In the obesity dataset, an 80:20 ratio resulted in an accuracy rate of up to 97%.Keywords: Decision Tree, LightGBM, Obesity, Data Mining, Classification
Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu PUTRA, KURNIA RAMADHAN; RACHMAN, MOHAMMAD ADITIYA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.179-193

Abstract

AbstrakSistem rekomendasi dapat dimanfaatkan untuk membantu pengguna menemukan item atau informasi sesuai preferensi mereka, termasuk lagu. Metode seperti Collaborative Filtering (CF), Content-Based Filtering (CBF), dan Hybrid Filtering digunakan untuk meningkatkan kualitas rekomendasi berdasarkan interaksi pengguna dan karakteristik konten. Penelitian ini membandingkan efektivitas ketiga metode tersebut dalam rekomendasi lagu menggunakan dataset dengan 68.330 entri data. Metode CF dan CBF diterapkan secara terpisah, lalu dikombinasikan dalam pendekatan hybrid untuk mengevaluasi peningkatan hasil. CF mencapai presisi 49.9%, CBF 39.5%, sedangkan hybrid CF-CBF mencatat presisi tertinggi sebesar 50.7%. Sebaliknya, hybrid CBF-CF menghasilkan presisi terendah, yaitu 38.4%. Kesimpulannya, pendekatan hybrid CF-CBF lebih unggul dalam merekomendasikan lagu sesuai preferensi pengguna dibandingkan metode lainnya.Kata kunci: sistem rekomendasi, rekomendasi lagu, content-based filtering, collaborative filtering, hybrid filtering AbstractRecommender systems can be utilized to assist users in discovering items or information that align with their preferences, including music. Methods such as Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering enhance recommendation quality based on user interactions and content characteristics. This study compares the effectiveness of these three methods in music recommendation using a dataset containing 68,330 entries. CF and CBF were implemented separately and combined in a hybrid approach to evaluate performance improvements. CF achieved a precision of 49.9% and CBF 39.5%, while the hybrid CF-CBF approach recorded the highest precision at 50.7%. In contrast, the hybrid CBF-CF approach yielded the lowest precision, at 38.4%. In conclusion, the hybrid CF-CBF approach outperforms other methods in delivering music recommendations tailored to user preferences.Keywords: recommendation system, song recommendation, content-based filtering, collaborative filtering, hybrid filtering
Renovator: Resilience System in Microservices Architecture Using Fault Tolerance Factor PUTRA, KURNIA RAMADHAN; UMAROH, SOFIA; FAHRUDIN, NUR FITRIANTI; PANDJI, PRAMBUDHI WIBOWO
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.235-249

Abstract

ABSTRAKArsitektur microservices menghadapi tantangan dalam menjaga ketahanan layanan akibat cascading failure, ketergantungan antarlayanan yang dinamis, serta keterbatasan mekanisme fault tolerance berbasis ambang statis. Pendekatan seperti circuit breaker dan bulkhead hanya memberi perlindungan terbatas ketika beban dan latensi berubah cepat. Untuk menjawab gap tersebut, penelitian ini memperkenalkan Renovator, kerangka kerja ketahanan yang memperluas circuit breaker melalui pemantauan adaptif dan pemulihan otomatis. Evaluasi pada simulasi Sistem Presensi dengan empat skenario yaitu cascading failure, latency spike, normal load, dan single-service failure menunjukkan peningkatan signifikan dibandingkan baseline: ketersediaan naik (86,31% menjadi 93,95%), MTTR berkurang 49–67%, tingkat kesalahan turun 55–63%, serta latensi membaik 20–27% tanpa memengaruhi throughput. Kontribusi utama penelitian ini adalah pengembangan Renovator sebagai mekanisme circuit breaker yang lebih adaptif dan otomatis untuk meningkatkan ketahanan microservices.Kata kunci: microservices, fault tolerance, circuit breaker, MSA resilienceABSTRACTMicroservices architectures face resilience challenges due to cascading failures, dynamic dependencies, and the limitations of fault tolerance mechanisms that rely on static thresholds. Techniques such as Circuit Breaker and Bulkhead provide only partial protection under rapidly changing workloads. To address this gap, this study introduces Renovator, a resilience framework that enhances circuit breaker functionality through adaptive monitoring and automated recovery. Evaluated on a simulated Attendance System under four scenarios—cascading failure, latency spike, normal load, and single-service failure, Renovator shows notable improvements over the baseline: availability increases (from 86.31% to 93.95%), MTTR decreases by 49–67%, error rates drop 55–63%, and latency improves 20–27% with no throughput degradation. The main contribution is an adaptive and automated circuit-breaker–based framework to strengthen microservices resilience.Keywords: microservices, fault tolerance, circuit breaker, MSA resilience
Co-Authors Abigael, Hugo Adinda, Najwa Angelica, Evlyn Catur Candra, Muhammad Zuhri FAHRUDIN, NUR FITRIANTI Haniifah, Aldira Fitri LAUTAN, GAMAS BLOORY Nurjaman, Asep Rizal Pandji, Prambudhi Wibowo Purwanti, Tri Rahayu Putri, Azzahra Candra Putri, Windi Laudi RACHMAN, MOHAMMAD ADITIYA Rahmainingtias, Berka Ridha Rahman, Ilham Fathur Reza, Flandi Muhamad Risyanti, Rifa Aulia Rizqullah, Bahy Tsany Umaroh, Sofia Utami, Azzila Deswita Wahidyah, Nazillah