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All Journal Media Statistika JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Information Systems and Technology Research Journal of International Conference Proceedings Jurnal Teknik Terapan (J-TETA) Journal of Data Mining and Information Systems Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Joong-Ki Jurnal Pengabdian Masyarakat SENSASI
Kartika Maulida Hindrayani
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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PEMODELAN SENTIMEN ULASAN PENGGUNA APLIKASI KURURIO DENGAN REGRESI LOGISTIK MENGG Arkananta Handoyo; Imam; Kartika Maulida Hindrayani; Shindi Shella May Wara
PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer Vol. 9 No. 2 (2025): Jurnal PINTER
Publisher : PTIK Fakultas Teknik UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/pinter.9.2.13

Abstract

Kururio is a local online transportation application that currently does not have as many users as its competitors. This study aims to understand user perceptions of the Kururio application through sentiment analysis of user reviews on the Google Play Store. The method used is sentiment classification using Logistic Regression on PySpark, with the support of preprocessing using the Sastrawi library. The research stages include review data scraping, data preprocessing (case folding, cleansing, tokenization, stopword removal, stemming), TF-IDF weighting, modeling, evaluation, and k-fold cross-validation. A comparison was made between Logistic Regression, Naive Bayes, Decision Tree, and Random Forest modeling algorithms. The results show that Logistic Regression outperformed the other models, with the optimal train-test split at 70:30. The analysis also revealed that positive reviews were more dominant than negative ones. Therefore, in general, users have a favorable perception of the application, although there are still several aspects that need improvement.
X-Means Clustering Algorithm in Property Customer Payment Pattern Edelin Fortuna; Dwi Arman Prasetya; Kartika Maulida Hindrayani
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1228

Abstract

Understanding customer behavior is essential for ensuring the sustainability and competitiveness of property businesses. This study aims to segment customers of PT X based on installment payment patterns using the X-Means clustering algorithm, which automatically determines the optimal number of clusters. From 9,615 transaction records, 386 customer profiles were analyzed using four features: number of transactions, number of late payments, payment differences, and payment status. The analysis produced five customer clusters with a silhouette score of 0.571, reflecting good cluster separation and internal consistency. The results reveal distinct payment behaviors, such as customers who consistently pay on time, those frequently late, and those who have fully completed their payments. These clusters provide practical insights that can support targeted communication, billing, and retention strategies. Furthermore, the study highlights the effectiveness of adaptive clustering techniques in improving segmentation accuracy. The findings contribute to data-driven decision-making in customer management, offering valuable guidance for enhancing operational efficiency and supporting long-term business performance.
Analisis Sentimen Ulasan Aplikasi Maxim Merchant dengan Support Vector Machine (SVM) dan Random Forest Selly Rizkiyah; Indira Zein Rizqin; Milla Akbarany Baktiar Putri; Shindi Shella May Wara; Kartika Maulida Hindrayani
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.4765

Abstract

The development of digital technology, especially mobile devices, has led to an increase in application-based services. One important aspect in app development is to deeply understand user perception and satisfaction. This study aims to analyze user sentiment towards the Maxim Merchant application based on reviews obtained from the Google Play Store platform. A total of more than 2800 Indonesian-language reviews were collected using web scraping techniques. The review data was processed through pre-processing stages such as text cleaning, normalization, tokenization, removal of unimportant words, and stemming. Sentiments are categorized into positive and negative based on the review score, where scores of 1 to 3 are considered negative, and scores of 4 and 5 are considered positive. Word cloud visualization is used to show the dominant words of each sentiment category. The data is then converted into numerical form using TF-IDF and selected using the Chi-Square method. Classification was performed using Support Vector Machine and Random Forest algorithms. The evaluation results show that the Support Vector Machine algorithm performs better in classifying sentiment, especially in handling high-dimensional text data.
Interpretive Structural Modeling-Based Decision Support System for Marine Tourism Strategy Kartini; Kartika Maulida Hindrayani; Endang Tri Wahyurini; Aang Kisnu Darmawan; Hilya Zada Mardhatilla Al Haadiy; Maudi Adella; Rizky Fatkhur Rohman
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.649

Abstract

Marine tourism in Madura has great potential for economic growth, but its unsustainable management threatens the ecosystem and community welfare. A development strategy is needed that balances economic, social, and environmental aspects. The main challenge is the complexity of sustainable marine tourism development, where various factors are interrelated and require a holistic approach. Previous studies have identified factors that influence marine tourism, but have been lacking in integrating them into a comprehensive decision-making framework. This study aims to fill this gap by developing a Decision Support System (DSS) to help stakeholders formulate sustainable marine tourism development strategies. The main objective of this study is to develop a DSS based on Interpretive Structural Modeling (ISM) to map the relationships between key variables and provide strategy recommendations. The ISM approach is used to identify, analyze, and interpret the relationships between key variables. Data were collected through expert interviews, surveys, and literature studies. The study produced a hierarchical model that describes the influence and relationships between variables, as well as a DSS that is able to provide development strategy recommendations based on priorities and objectives. This study contributes to providing a structured and evidence-based decision-making tool for sustainable marine tourism development in Madura. The originality of this study lies in the integration of ISM into DSS for sustainable marine tourism, offering a new perspective in strategic decision-making.
Komparasi Gaussian Process Regression Dan Long Short-Term Memory Dalam Prediksi Harga Saham Tiga Bank BUMN Kristananda, Raja Valentino; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Volatilitas saham bank BUMN berdampak signifikan terhadap stabilitas ekonomi Indonesia dan keputusan investor. Prediksi harga saham yang akurat dapat membantu investor dan pemangku kepentingan dalam membuat keputusan yang lebih baik. Penelitian sebelumnya menggunakan Support Vector Regression (SVR) untuk prediksi saham Bank BRI menghasilkan MAPE sebesar 4,61%, menunjukkan ruang untuk pengembangan metode yang lebih akurat. Penelitian ini bertujuan membandingkan efektivitas pendekatan probabilistik non-parametrik yaitu Gaussian Process Regression (GPR) dengan berbagai kernel terhadap pendekatan deep learning sequential yaitu Long Short-Term Memory (LSTM) dalam memprediksi harga saham tiga bank BUMN (BMRI, BBRI, BBNI). Perbandingan kedua pendekatan ini penting untuk mengevaluasi keunggulan masing-masing metode dalam konteks prediksi finansial yang kompleks. Penelitian ini menggunakan data historis harga saham dari September 2018 hingga September 2024. Data dibagi menjadi 80% untuk pelatihan, 10% untuk validasi, dan 10% untuk pengujian. Model GPR diimplementasikan dengan empat kernel (RBF, Matern, Rational Quadratic, dan Linear), sementara LSTM diimplementasikan dengan empat variasi (LSTM_50, LSTM_100, LSTM_50_higher_dropout, dan LSTM_stacked). Hasil menunjukkan bahwa model GPR dengan kernel Matern memberikan kinerja terbaik untuk ketiga saham bank dengan nilai RMSE terendah (BMRI: 88,34, BBRI: 73,94, BBNI: 70,38) dan skor R² tertinggi (BMRI: 0,92, BBRI: 0,90, BBNI: 0,96). Sementara itu, model LSTM terbaik menunjukkan kinerja lebih rendah dengan RMSE untuk BMRI: 116,80, BBRI: 111,67, dan BBNI: 94,46. Secara keseluruhan GPR dengan Kernel Matern mengungguli LSTM dalam memprediksi harga saham bank BUMN, memberikan landasan kuat untuk pengembangan sistem pendukung keputusan investasi di masa depan.   Abstract Accurate stock price prediction can assist investors and stakeholders in making better decisions. Previous research using Support Vector Regression (SVR) for Bank BRI stock prediction achieved a MAPE of 4.61%, indicating room for developing more accurate methods. This study aims to compare the effectiveness of a non-parametric probabilistic approach, namely Gaussian Process Regression (GPR) with various kernels, against a sequential deep learning approach, namely Long Short-Term Memory (LSTM), in predicting stock prices of three state-owned banks (BMRI, BBRI, BBNI). The comparison of these two approaches is important for evaluating the advantages of each method in the context of complex financial prediction. This study uses historical stock price data from September 2018 to September 2024. The data is divided into 80% for training, 10% for validation, and 10% for testing. The GPR model is implemented with four kernels (RBF, Matern, Rational Quadratic, and Linear), while LSTM is implemented with four variations (LSTM_50, LSTM_100, LSTM_50_higher_dropout, and LSTM_stacked). Results show that the GPR model with Matern kernel provides the best performance for all three bank stocks with the lowest RMSE values (BMRI: 88.34, BBRI: 73.94, BBNI: 70.38) and highest R² scores (BMRI: 0.9254, BBRI: 0.9089, BBNI: 0.9685). Meanwhile, the best LSTM models show lower performance with RMSE for BMRI: 116.80, BBRI: 111.67, and BBNI: 94.46. In conclusion, GPR with Matern Kernel outperforms LSTM in predicting state-owned bank stock prices, providing a strong foundation for developing investment decision support systems in the future.
Klasifikasi Gerakan Bahasa Isyarat Indonesia (Bisindo) menggunakan Arsitektur Transfer Learning Xception Meisya Vira Amelia; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15674

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 98% during testing
Pengujian Fungsional Website Crusher Report Berbasis Machine Learning Menggunakan Metode Robustness Testing Chelsea Ayu Adhigiadany; Kartika Maulida Hindrayani; Dwi Arman Prasetya
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2014

Abstract

Website dan Machine Learning menjadi kebutuhan penting perusahaan dalam rangka meningkatkan efektivitas kinerja. Salah satu implementasi integrasi website dengan Machine Learning adalah website Crusher Report milik PT XYZ. Website yang dirancang dengan memanfaatkan LARS, PostgreSQL, dan Flask ini sudah diuji secara ketangkasan model dalam memprediksi. Penelitian ini bertujuan untuk menguji keandalan website Crusher Report sebagai user interface milik PT XYZ menggunakan pendekatan Black Box Testing dengan metode Robustness Testing. Skenario pengujian yang digunakan yaitu dengan memberikan input diluar ketentuan website. Hasil pengujian menunjukkan bahwa website mampu menangani seluruh input tidak valid dengan baik melalui notifikasi kesalahan dan pengaturan nilai input otomatis, menghasilkan tingkat keberhasilan pengujian sebesar 100%. Temuan ini menunjukkan bahwa website Crusher Report efektif dalam mendeteksi dan mengelola kesalahan input, serta layak digunakan sebagai platform pendukung operasional crusher PT XYZ.
Implementasi Metode Ensemble ROCK dalam Pengelompokan UMKM di Kabupaten Malang Reza Sadiya Purwadwika; Kartika Maulida Hindrayani; Aviolla Terza Damaliana
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.3396

Abstract

UMKM memiliki peran penting dalam perekonomian nasional, namun masih menghadapi berbagai permasalahan seperti rendahnya pemanfaatan teknologi, keterbatasan akses permodalan, dan lemahnya daya saing. Kompleksitas karakteristik data UMKM yang mencakup variabel numerik dan kategorikal menjadi tantangan dalam analisis dan pemetaan yang akurat. Penelitian ini bertujuan untuk mengelompokkan UMKM di Kabupaten Malang berdasarkan karakteristik usaha dan pelaku usahanya dengan pendekatan ensemble clustering menggunakan algoritma ROCK. Data terdiri dari 75 entri UMKM yang mencakup variabel numerik (omset, modal, tenaga kerja) dan kategorikal (jenis usaha, penggunaan aplikasi transportasi daring). Clustering dilakukan secara terpisah dengan Agglomerative Hierarchical Clustering untuk data numerik dan ROCK untuk data kategorikal. Hasil kedua metode digabungkan menggunakan pendekatan ensemble untuk memperoleh klaster yang lebih stabil dan representatif. Parameter optimal diperoleh pada theta = 0,05 dan k = 4 dengan nilai Clustering Purity (CP*) sebesar 0,8148 dan Davies-Bouldin Index sebesar 0,3817, menunjukkan pemisahan cluster yang baik. Cluster akhir menunjukkan perbedaan signifikan dalam skala usaha, pemanfaatan teknologi digital, dan performa ekonomi. Temuan ini diharapkan menjadi dasar dalam merancang kebijakan pengembangan UMKM yang lebih tepat sasaran dan berbasis data.
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Selena Nurmanina Afandy; Kartika Maulida Hindrayani; Aviolla Terza Damaliana
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.v8i3.3501

Abstract

This study presents a structured comparative evaluation of IndoBERT and Multilingual BERT (mBERT) for three-class sentiment classification of customer reviews from Pawonkoe Banyuwangi, an Indonesian small and medium-sized enterprise (SME). Motivated by the limited transferability of IndoNLU-style benchmarks to real SME feedback, the central question is whether monolingual versus multilingual transformers remain reliable when fine-tuned on small, domain-specific, and operationally noisy datasets. A total of 365 survey-based reviews (January–December 2024), which is substantially smaller than typical transformer fine-tuning corpora, served as the empirical basis. Models were fine-tuned under matched hyperparameters and evaluated using a single stratified hold-out train–test split (not cross-validation), reporting accuracy, precision, recall, and F1-score. To reflect the deployed pipeline, mBERT additionally incorporates the original 1–5 rating as an auxiliary numeric signal alongside the review text, whereas IndoBERT is trained on text only. The results reveal a substantial performance gap: mBERT achieved 81% test accuracy, whereas IndoBERT reached 48% under the same evaluation setting. Because the label distribution is strongly imbalanced (with very few negative instances), these aggregate scores should be interpreted as overall effectiveness rather than minority-class robustness. Overall, the findings indicate that multilingual representations combined with auxiliary rating information can generalize more effectively in low-resource SME scenarios, while IndoBERT appears more sensitive to data scarcity in this context. The study offers practical guidance for model selection in resource-constrained Indonesian sentiment analytics and contributes evidence on transformer behavior beyond curated benchmarks.
Optimisation of Hyperparameter Tuning and Optimiser on MobileNetV2 for Batik Parang Classification Muhammad Rafli; Dwi Arman Prasetya; Kartika Maulida Hindrayani
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.
Co-Authors Aang Kisnu Darmawan Abdul Mukti Achmad Dzulfiqar Alfiansyah Adhigiadany, Chelsea Ayu Afidria, Zulfa Febi Ahmad, Davin Anezta Aisyah Kirana Putri Isyanto Aji R, Prismahardi Altetiko, Faizal Johan Alya Mirza Safira Alzam, Muhammad Arsyad Amanda Aulia Amelia, Meisya Vira Amri Muhaimin Ardia Eva Ardiani Arkananta Handoyo Aulia Nur Fitriani Aviolla Terza Damaliana Azizah Zalfa Assyadida Azizah, Alisa Jihan Betty Dewi Puspasari Bhalqis, Anissa Andiar Brescia Ayundina Yuniarossy Budi, Aditya Septa Burhan Syarif Acarya Chelsea Ayu Adhigiadany Christina Halim Christina, Enzelica Vica Damaliana, Aviolla Terza Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edelin Fortuna Elmaliyasari, Shifa Endang Tri Wahyurini Fahrudin, Tresna Maulana Fajar Ramadhani Fajria Ulumin Nafiah Fernando, Moch. Firman Hilya Zada Mardhatilla Al Haadiy Holly Patrycia I Gede Susrama Mas Diayasa idhom, Mohammad Imam Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Isyanto, Aisyah Kirana Putri Kartini Kartini Kartini Kartini Khairunisa, Adenda Kristananda, Raja Valentino Lidya Musaffak, Awal Made Hanindia Prami Swari Maudi Adella Maulana F, Tresna Meisya Vira Amelia Meisya Vira Amelia Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhammad Rafli Muhimmatul Arofah Nanda Kurnia Wardati Ni Luh Ayu Nariswari Dewi Ningrum, Imelda Widya Ningrum, Lisya Septyo Nur Aini Rakhmawati Pakpahan, Vera Febrianti Pratiwi, Nanda Aulia Prismahardi Aji Riyantoko Purwadwika, Reza Sadiya Putro, R. Kokoh H. rachmanto, Nugroho Fajar Radya Ardi Renaldy Al Ikhsan Reza Sadiya Purwadwika Rhomaningtias, Lina Riskiyah, Ameliyah Risnaldy Novendra Irawan Rizky Fatkhur Rohman Safira, Alya Mirza Safitri, Eristya Maya Saputra, Wahyu S. J. Selena Nurmanina Afandy Selly Rizkiyah Shindi Shella May Wara Shindi Shella May Wara Shindi Shella May Wara Sinthya Putri, Diana Steffany Marcellia Witanto Thoriqulhaq, Muhammad Tresna Maulana F Tresna Maulana Fahruddin Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono Trimono, Trimono Wahyu Syaifullah Jauharis Saputra Wahyu Syaifullah JS Wibowo, Muhammad Bagas Satrio Yosua Satria Bara Harmoni