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Journal : bit-Tech

Optimisation of Hyperparameter Tuning and Optimiser on MobileNetV2 for Batik Parang Classification Rafli, Muhammad; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3576

Abstract

Batik Parang is a prominent traditional motif in Indonesia, characterised by repetitive diagonal patterns and subtle visual variations across regional styles, such as Solo Parang and Yogyakarta Parang, which pose challenges for automated image classification. This study addresses this challenge by introducing an optimisation-focused framework that integrates hyperparameter tuning strategies with a lightweight convolutional neural network, extending the practical use of MobileNetV2 for fine-grained cultural motif classification. A balanced dataset of 160 batik images collected from Kaggle was employed and partitioned using an 80:20 stratified split to ensure class consistency. The model was evaluated on a limited yet representative dataset reflecting realistic small-scale cultural heritage scenarios. Two hyperparameter tuning methods, Bayesian Optimisation and Particle Swarm Optimisation, were applied to optimise learning rate, batch size, and dropout rate, while two optimisers, Adam and Adagrad, were compared to analyse their effects on convergence stability and generalisation. The training process followed a two-phase strategy consisting of transfer learning and selective fine-tuning of upper MobileNetV2 layers. Experimental results indicate that Adagrad-based configurations consistently outperform Adam-based models, which exhibited class collapse and poor generalisation. The optimal configuration, combining Adagrad with Bayesian Optimisation, achieved a validation accuracy of 91% with balanced precision, recall, and F1-score across both Parang classes. These findings demonstrate that careful optimisation enhances the reliability of lightweight CNNs and support extending the proposed framework to other cultural heritage classification tasks and resource-constrained real-time applications.
Stacked LSTM Integrated with Big Data Pipelines for Automated Food Beverage Stock Price Prediction Asfiani, Ilil Musyarof; Prasetya, Dwi Arman; Trimono, Trimono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3687

Abstract

Stock price volatility in the Food and Beverage (F&B) sector presents persistent challenges for investors and decision-makers, particularly in emerging markets. This study proposes an automated stock price prediction framework whose primary contribution lies in the system-level integration of a Stacked Long Short-Term Memory (LSTM) model with a scalable big data orchestration pipeline, rather than in introducing a new forecasting algorithm alone. The system targets three Indonesian F&B companies PT Indofood CBP Sukses Makmur Tbk, PT Mayora Indah Tbk, and PT Garudafood Putra Putri Jaya Tbk using historical daily stock price data. The dataset spans multiple years of trading records retrieved from the Yahoo Finance API, and predictions are generated for a seven-day forecasting horizon. Methodologically, the approach combines a multi-layer LSTM architecture with Apache Spark for distributed data preprocessing, Apache Airflow for automated workflow orchestration, and PostgreSQL for structured data storage. This integration enables scheduled data ingestion, reproducible model training, and continuous forecasting within an end-to-end analytics pipeline. Model performance is evaluated using error-based metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), and is benchmarked against a conventional single-layer LSTM without pipeline orchestration. Empirical results show that the proposed pipeline-based Stacked LSTM achieves lower prediction error, with MAPE values ranging between approximately 1.1% and 2.2% across the evaluated stocks, indicating improved stability and accuracy. Overall, the findings demonstrate enhanced forecasting reliability and deployment readiness through automated pipelines.
Co-Authors ', Nachrowie ., Humaidi A. A. Ngurah Gunawan Aan Nehru Awanto Achmad Junaidi Adelia Yuandhika Adhigiadany, Chelsea Ayu Aditya, Wigananda Firdaus Putra Afidria, Zulfa Febi Agustin, Sesillia Akio Kitagawa Alam, Fajar Indra Nur Alfa, Aniysah Fauziyyah Alhamda, Denisa Septalian Ali, Munawar Amrullah, Ahmad Wildan Andre Leto Andrew Arjunanda Yasin Anggraini Puspita Sari Anindha Lazuardi Aries Boedi Setiawan Arifani, Kahpi Baiquni Arifuddin, Rahman Arinda, Putri Surya Arum Puspita Ayu Aryananda, Rangga Laksana Asfiani, Ilil Musyarof Atiana Sofia Kaci Aviolla Terza Damaliana Awang, Wan Suryani Wan Azizah, Alisa Jihan Baidowi Baidowi Baidowi Baidowi Bambang Nurdewanto Barus, Indra Basitha F Hidayatulail Cahya Eka Melati Cahyani Kuswardhani, Hajjar Ayu cahyono, wahyu eko Candra Laksana Dafa Zain Musyafa Damai Arbaus, Damai Damaliana, Aviolla Terza Danang - Destiawan Danang Destiawan Datia Putri Nabila Br Tarigan Desi Tristianti Desyderius Minggu Dicky Kurniawan Diyasa, I Gede Susrama Mas Dody Pintarko Dwi Agung Ayubi E, Nachrowie Eka Prakarsa Mandyartha Ekawati, Anies Eko Wahyu Prasetyo Elta Sonalitha Sonalitha Emilia, Kholidatus Erik Roma Hurmuzi Erika Fatimatul Hidayanti Fadlila Agustina Fahrudin, Tresna Maulana Farhans, Muhammad Izzudin Febriyanti, Alvi Yuana Firdaus Firdaus Firza Prima Aditiawan Fitrah, Hazza Gatut Yulisusianto Halim, Christina Hari Fitria Windi Hendry Yudha Pratama Herdianti, Rahmalia Anindya Hesti Sholikah, Hesti Hidayatulail, Basitha F Hikmata Tartila Hiroshi Suzuki Hurmuzi, Erik Roma I Gede Susrama Mas Diyasa Ibrahim, Mohd Zamri Bin idhom, Mohammad Indra Barus Irsyadi, Muhamad Haidir Ismail, Jefri Abdurrozak Januar, Teddy Jariyah Jeki Saputra Junita Junita Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Kholid, Fajar Kukuh Yudhistiro, Kukuh Kurniawan, Dicky Kusuma, Dwi Febri Chandra Kusuma, Firdaus Miftakh Kuswardana, Dendy Arizki Laksana, Candra Larasati Lestari, Amanda Ayu Dewi Lisanthoni, Angela Luqna Aziziyah Maulidiyyah, Nova Auliyatul Millani, Alief Indy Mohammad Ansori Mohammad Idhom Mohammad, Bawazir Fadhil Muhaimin, Amri Muhammad Ansori Muhammad Ghinan Navsih Muhammad Muharrom Al Haromainy Muhammad Naswan Izzudin Akmal Mulyadi Mulyadi Nachrowie Nachrowie Nachrowie, Nachrowie Nambo Hidetaka Narumi Hayakawa Nauval Theo Jovaldi Nezalfa Sabrina Niken Sulistyowati Ningrum, Imelda Widya Ninik Sisharini Ninis Herawati Norma Windiyanti Novita Anggraini Nur Rachman Nur Rachman Supatmana Muda Nur Rochman Nur Rochman Permana, Iwan Setiawan Prakoso, Akbar Tri Prameswari, Diajeng Prismahardi Aji Riyantoko Puput Dani Prasetyo Adi Puput Marina Azlia Sari Putri Lestari Putri, Irma Amanda Putri, Serlinda Mareta Rabi, Abd. Rafli, Muhammad Rahayu Sri Utami Rahayu, Ayu Sri Rahman Arifuddin Rahmanda Putri, Endin Rahmawati, Adinda Aulia Respati Respati Ristiyani, Sintiya Riyantoko, Prismahardi Aji Rosariawari, Firra Rudi Wilson Sagita Rochman Salim, Hotimah Masdan Santika, Surya Sari, Andina Paramita Sigit, Syauqita Siswanto Siswanto Sitanggang, Desi Daomara Siti Nuurlaily Rukmana, Siti Nuurlaily Stanislaus Yoseph Subairi Subairi Sugiarto S Sumartono Sumartono Sumartono Suprayogi Suprayogi Suprayogi Suprayogi Surya Nanda Santika, Surya Suryantari, Putu Anggi Takahiro Kitajima Takashi Yasuno Tresna Maulana Fahrudin Trimono Trimono Trimono, Trimono Utomo, Setyobudi Wahyu Dirgantara Wahyu Putra Pratama Wahyu Syaifullah Jauharis Saputra Wahyuni, Dinar H S wangge, ferdinandus Weisrawei, Yosef Yasin, Andrew Arjunanda Yohanes U D Sipul Yosef Weisrawei Yosua Satria Bara Harmoni Yuliani, Devina Putri Yunia Dwie Nurchayanie Yusaq Tomo Ardianto