Abstract:Abstract: Digital banking has brought various innovations in financial services, one of which is Blu Deposito by BCA Digital. However, the adoption rate of digital deposit services is still relatively low compared to digital…
ital payment services. This study aims to identify and analyze the factors that influence customers' intentions and actual behavior in using Blu Deposito with reference to the Technology Acceptance Model (TAM). This study aims to analyze the factors that influence customers' intentions and actual behavior in adopting Blu Deposito using the Technology Acceptance Model (TAM) framework. Data was collected through a Google Form questionnaire from 54 customers at one BCA branch and analyzed using SPSS through validity and reliability tests, descriptive analysis, and multiple regression. The results show that Behavioral Intention (BI)is significantly influenced by Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Attitude Toward Using (ATU), with PEOU as the most dominant factor. In addition, BI has a significant effect on Actual System Use (AU), which confirms the relevance of applying the TAM model in the context of digital deposit products. These findings indicate that ease of use plays a greater role than financial benefits in encouraging users to adopt Blu Deposits. This study contributes to the understanding of digital deposit adoption and provides managerial insights to improve the usability and user engagement of digital banking services.
Keywords: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model
Abstrak: Perbankan digital telah menghadirkan berbagai inovasi dalam layanan keuangan, salah satunya Blu Deposito oleh BCA Digital. Meskipun demikian, tingkat adopsi terhadap layanan deposito digital masih relatif rendah dibandingkan dengan layanan pembayaran digital. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis faktor-faktor yang memengaruhi niat serta perilaku aktual nasabah dalam menggunakan Blu Deposito dengan mengacu pada kerangka Technology Acceptance Model (TAM). Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi niat dan perilaku aktual nasabah dalam mengadopsi Blu Deposito dengan menggunakan kerangka Technology Acceptance Model (TAM). Data dikumpulkan melalui kuesioner Google Form dari 54 nasabah di satu cabang BCA dan dianalisis menggunakan SPSS melalui uji validitas, reliabilitas, analisis deskriptif, dan regresi berganda. Hasil penelitian menunjukkan bahwa Behavioral Intention (BI) dipengaruhi secara signifikan oleh Perceived Ease of Use (PEOU), Perceived Usefulness (PU), dan Attitude Toward Using (ATU), dengan PEOU sebagai faktor paling dominan. Selain itu, BI berpengaruh signifikan terhadap Actual System Use (AU), yang menegaskan relevansi penerapan model TAM pada konteks produk deposito digital. Temuan ini menunjukkan bahwa kemudahan penggunaan memiliki peran lebih besar dibandingkan manfaat finansial dalam mendorong pengguna untuk mengadopsi Blu Deposito. Penelitian ini berkontribusi terhadap pemahaman adopsi deposito digital serta memberikan wawasan manajerial untuk meningkatkan kegunaan dan keterlibatan pengguna pada layanan perbankan digital.
Kata kunci: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model
Abstract:Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement…
nt of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions.
Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement
Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah.
Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa
Abstract:Abstract: Manual management of domain validity periods and SSL certificates is prone to human error and can cause service disruptions, as was the case at PT XYZ. A reactive approach that relies on vendor notifications has…
s proven to be insufficient to ensure operational continuity. This research aims to design and implement an automated monitoring system to transform this manual approach into a preventive and proactive security framework. The method used is the implementation of an open-source stack consisting of Prometheus to collect metrics from specialized exporters (Blackbox and Domain Exporter), and Grafana for informative centralized dashboard visualization. The system is also integrated with early warning notifications via Telegram for rapid incident response. The result is a functional system with a centralized dashboard that visually displays the remaining validity period of assets using color markers (green for safe status, yellow for early warning, and red for critical status). System testing showed very high accuracy, reaching 100% for domains (MAE 0 days) and 99.45% for SSL certificates (MAE 1.0 days). This system has successfully transformed manual processes into automated and preventive ones, significantly mitigating the risk of human error and ensuring the reliability of digital services.
Keywords: domain; grafana; monitoring; prometheus; SSL certificate.
Abstrak: Pengelolaan manual masa berlaku domain dan sertifikat SSL rentan terhadap human error dan dapat menyebabkan gangguan layanan, seperti yang pernah terjadi di PT XYZ. Pendekatan reaktif yang mengandalkan notifikasi vendor terbukti tidak lagi memadai untuk menjamin kontinuitas operasional. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pemantauan otomatis untuk mentransformasi pendekatan manual tersebut menjadi kerangka kerja keamanan yang preventif dan proaktif. Metode yang digunakan adalah implementasi stack open-source yang terdiri dari Prometheus untuk mengumpulkan metrik dari exporter spesialis (Blackbox dan Domain Exporter), serta Grafana untuk visualisasi dasbor terpusat yang informatif. Sistem ini juga diintegrasikan dengan notifikasi peringatan dini melalui Telegram untuk respons insiden yang cepat. Hasilnya adalah sebuah sistem fungsional dengan dashboard terpusat yang menampilkan sisa masa berlaku aset secara visual menggunakan penanda warna (hijau untuk status aman, kuning untuk peringatan dini, dan merah untuk status kritis). Pengujian sistem menunjukkan akurasi yang sangat tinggi, mencapai 100% untuk domain (MAE 0 hari) dan 99.45% untuk sertifikat SSL (MAE 1.0 hari). Sistem ini berhasil mengubah proses manual menjadi otomatis dan preventif, secara signifikan memitigasi risiko human error dan menjamin keandalan layanan digital.
Kata kunci: domain; grafana; pemantauan; prometheus; sertifikat SSL.
Abstract:Abstract: The policy to increase the Rural and Urban Land and Building Tax (PBB-P2) in Indonesia often elicits mixed reactions from the public. Some support it because they believe it can strengthen regional fiscal capacity,…
ity, while others reject it because they are concerned that it will increase the economic burden on the community. Understanding public sentiment towards this policy is important for evaluating the effectiveness of the policy and formulating appropriate communication strategies. This study aims to analyze public sentiment towards the PBB-P2 increase policy using data uploaded on Platform X (Twitter). The data were collected through crawling with the keyword “building tax,” then processed through several preprocessing stages before classifying tweets into positive and negative sentiments. Two models were used: Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM). Results show that SVM outperformed BiLSTM, achieving training accuracy of 99.4% and testing accuracy of 85.9%, with accuracy 0.8595, precision 0.8536, recall 0.8595, and F1-score 0.8449. Meanwhile, BiLSTM achieved training accuracy of 86.9% and testing accuracy of 82.9%, with accuracy 0.8294, precision 0.8150, recall 0.8294, and F1-score 0.8080. These findings suggest SVM is more effective in classifying public sentiment and can support better evaluation of regional tax policies.
Keywords: sentiment analysis; PBB-P2; BiLSTM; SVM; X platform
Abstrak: Kebijakan kenaikan tarif Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) di In-donesia sering memunculkan beragam reaksi dari masyarakat. Sebagian mendukung karena dianggap dapat memperkuat kapasitas fiskal daerah, sementara lainnya menolak karena kha-watir menambah beban ekonomi masyarakat. Pemahaman terhadap sentimen publik atas ke-bijakan tersebut penting untuk mengevaluasi efektivitas kebijakan dan merumuskan strategi komunikasi yang tepat. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap kebijakan kenaikan PBB-P2 menggunakan data unggahan di Platform X (Twitter). Data dik-umpulkan melalui proses crawling dengan kata kunci “pajak bangunan” kemudian diproses melalui beberapa tahap preprocessing sebelum diklasifikasikan menjadi sentimen positif dan negatif. Dua model digunakan dalam penelitian ini, yaitu Support Vector Machine (SVM) dan Bidirectional Long Short-Term Memory (BiLSTM). Hasil penelitian menunjukkan bahwa SVM memiliki kinerja lebih baik dibandingkan BiLSTM, dengan akurasi pelatihan 99,4% dan akurasi pengujian 85,9%. Nilai akurasi 0,8595, precision 0,8536, recall 0,8595, dan F1-score 0,8449. Sementara itu, BiLSTM memperoleh akurasi pelatihan 86,9% dan akurasi pengujian 82,9%, dengan akurasi 0,8294, precision 0,8150; recall 0,8294; dan F1-score 0,8080. Temuan ini menunjukkan bahwa SVM lebih efektif dalam mengklasifikasikan sentimen publik serta dapat mendukung evaluasi kebijakan pajak daerah dengan lebih baik.
Kata kunci: analisis sentimen; PBB-P2; BiLSTM; SVM; platform X
Abstract:Abstract: The rapid growth of the digital music industry requires accurate music genre classification systems to enhance user experience in streaming services. This study compares a domain-specific Long Short-Term Memory…
(LSTM) network with three Large Language Models (LLMs)—HuBERT, WavLM, and WAV2Vec 2.0—for Music Genre Classification (MGC). The LSTM model was trained using Mel-spectrograms transformed from the GTZAN dataset, while the LLMs were fine-tuned using a smaller set of raw audio samples due to computational constraints. All models were tested on datasets with identical genre labels to ensure a fair evaluation. Results show that the LSTM model achieved the highest accuracy of 97.10%, outperforming HuBERT (86.00%), WavLM (83.00%), and WAV2Vec 2.0 (80.00%). The LSTM demonstrated superior generalization and stability without overfitting, while the LLMs struggled to differentiate between genres with similar acoustic characteristics. These findings indicate that general-purpose pre-trained models, although powerful, are less effective in music-specific tasks due to domain mismatch. Therefore, incorporating music-specific features and architectures remains essential for achieving higher accuracy and reliability in automatic genre classification systems.
Keywords: audio large language models; comparative deep learning; music genre classification.
Abstrak: Pertumbuhan industri musik digital yang pesat menuntut sistem klasifikasi genre musik yang akurat untuk meningkatkan pengalaman pengguna dalam layanan streaming. Penelitian ini dilatarbelakangi oleh perkembangan pesat model pembelajaran mendalam, khususnya jaringan LSTM dan model bahasa berskala besar LLM seperti HuBERT, WavLM, dan WAV2Vec 2.0, yang telah menunjukkan kemampuan representasi audio yang kuat. Tujuan penelitian ini ini membandingkan jaringan Long Short-Term Memory (LSTM) khusus domain dengan tiga model Large Language Models (LLM)—HuBERT, WavLM, dan WAV2Vec 2.0—untuk tugas Klasifikasi Genre Musik (MGC). Metode penelitian melibatkan pelatihan LSTM menggunakan data Mel-spectrogram hasil transformasi dari dataset GTZAN, sementara LLM disesuaikan (fine-tuning) menggunakan data audio mentah dalam jumlah lebih kecil karena keterbatasan komputasi. Seluruh model diuji pada dataset dengan label genre yang sama untuk memastikan evaluasi yang adil. Hasil penelitian menunjukkan bahwa model LSTM mencapai akurasi tertinggi sebesar 97,10%, sedangkan model HuBERT, WavLM, dan WAV2Vec 2.0 masing-masing memperoleh 86,00%, 83,00%, dan 80,00%. Model LSTM menunjukkan kemampuan generalisasi yang lebih baik tanpa overfitting, sedangkan model LLM cenderung kesulitan membedakan genre dengan karakteristik akustik yang mirip. Kesimpulan penelitian ini adalah ketidaksesuaian domain secara signifikan membatasi performa model umum saat diterapkan pada tugas berbasis musik. Oleh karena itu, penggunaan fitur dan arsitektur khusus musik sangat penting dalam membangun sistem klasifikasi genre yang lebih akurat.
Kata kunci: klasifikasi genre musik; model bahasa besar; perbandingan pembelajaran mendalam.
Abstract:Abstract: Soil moisture is an important factor in determining the watering needs of plants for optimal growth. Therefore, accurate monitoring of soil moisture is necessary. This research aims to design and build a soil moisture…
oisture detection tool based on the Decision Tree algorithm with the support of the YL-69 sensor for humidity measurement and the DHT11 sensor for temperature measurement to increase data accuracy. This system uses NodeMCU ESP8266 as a microcontroller and is integrated with an Android application as a user interface. Sensor interpretation data is analyzed using the Decision Tree algorithm to determine soil conditions (dry, damp or wet). The test results show an accuracy level of 95% from 300 data samples. Thus, this system is able to detect soil moisture effectively and can help increase the efficiency of crop management on a household and commercial agricultural scale.
Keywords: agriculture, android, decision tree algorithm, sensors, soil moisture detection
Abstract:Abstract: Asthma is a chronic respiratory disease that affects millions of people worldwide, making early detection crucial to prevent complications. This study aims to compare the performance of the Decision Tree and Random…
ndom Forest algorithms in classifying asthma based on clinical symptom data. The data were processed through feature selection and model training stages, then evaluated using accuracy, precision, recall, and F1-score.The experimental analysis revealed that the Random Forest algorithm surpassed the Decision Tree in all metrics, achieving 95.19% accuracy, 90.43% precision, 95.00% recall, and 93.00% F1-score. In contrast, the Decision Tree obtained 89.14% accuracy, 90.60% precision, 88.70% recall, and 89.70% F1-score. These results suggest that Random Forest is more robust and dependable, especially in managing complex and imbalanced medical datasets.
Keywords: asthma detection; decision tree; random forest; machine learning.
Abstrak: Asma merupakan penyakit pernapasan kronis yang memengaruhi jutaan orang di seluruh dunia sehingga deteksi dini sangat penting untuk mencegah komplikasi. Penelitian ini bertujuan membandingkan kinerja algoritma Decision Tree dan Random Forest dalam mengklasifikasikan asma berdasarkan data gejala klinis. Data diproses melalui tahapan seleksi fitur dan pelatihan model, kemudian dievaluasi menggunakan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 90.43%, presisi 95.00%, recall 95.00%, dan F1-score 93.00%. Sebaliknya, Decision Tree memperoleh akurasi 89.14%, presisi 90.60%, recall 88.70%, dan F1-score 89.70%. Hasil ini menunjukkan bahwa Random Forest lebih kuat dan dapat diandalkan, terutama dalam mengelola kumpulan data medis yang kompleks dan tidak seimbang.
Kata kunci: deteksi asma; decision tree; random forest; pembelajaran mesin.
Abstract:Abstract: The increasing use of digital banking applications has led to the need for a deeper understanding of user perceptions, especially through aspect-based sentiment analysis. This study aims to classify the sentiment…
nt of SeaBank app users by focusing on four main aspects: learnability, efficiency, technical issues or errors, and satisfaction. Review data totaling 1,971 comments were collected from the Google Play Store and labeled with sentiments based on the scores (ratings) given by users. The CRISP-DM approach serves as the methodological framework for this study, which includes five classification algorithms: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Random Forest. The evaluation results show that the SVM algorithm provides the best performance with the highest average value of the four aspects achieving accuracy of 93.91%, Precision of 91.16%, recall of 97.96% and F1-Measure of 94.33%. According to the research findings, the Support Vector Machine (SVM) algorithm provides the best performance when performing aspect-based sentiment analysis on text data from digital banking application reviews. The findings are expected to serve as a reference for the development of automated evaluation systems that rely on user opinions as the basis for decision making.
Keywords: aspects; CRISP-DM; digital Banking; seabank; sentiment analysis
Abstrak: Peningkatan pemakaian aplikasi perbankan digital mendorong perlunya pemahaman yang lebih dalam mengenai persepsi pengguna, terutama melalui analisis sentimen berbasis aspek. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi SeaBank dengan berfokus pada empat aspek utama: kemudahan dipelajari (learnability), efisiensi penggunaan (efficiency), kendala atau kesalahan teknis (error), serta tingkat kepuasan (satisfaction). Data ulasan berjumlah 1.971 komentar dikumpulkan dari Google Play Store dan diberi label sentimen berdasarkan skor (rating) yang diberikan oleh pengguna. Pendekatan CRISP-DM berfungsi sebagai kerangka metodologis untuk penelitian ini, yang mencakup lima algoritma klasifikasi: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, dan Random Forest. Hasil evaluasi menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan nilai rata-rata dari ke empat aspek tertinggi yang mencapai accuracy sebesar 93.91%, Precision sebesar 91.16%, recall sebesar 97.96% dan F1-Measure sebesar 94.33%. Menurut temuan penelitian, algoritma Support Vector Machine (SVM) memberikan kinerja terbaik saat melakukan analisis sentimen berbasis aspek pada data teks dari ulasan aplikasi Seabank. Temuan ini diharapkan dapat menjadi referensi bagi pengembangan sistem evaluasi otomatis yang mengandalkan opini pengguna sebagai dasar pengambilan keputusan.
Kata kunci: Analisis Sentimen, Aspek, Bank Digital, SeaBank, CRISP-DM
Abstract:Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative…
analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions.
Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE
Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis.
Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE
Abstract:Abstract: The rapid development of information and communication technology has prompted the government to provide digital-based services, including in the management of demographic data. This study aims to apply the Task-Centered…
k-Centered System Design (TCSD) method in designing the Muara Enim Regency Demographic Data Information System. The TCSD method was chosen to ensure that the prototype design process was systematic and focused on user needs and tasks. The research stages included identification, user-centered needs analysis, scenario-based design, and walkthrough evaluation. The designed prototype supports several main tasks, including viewing demographic statistics, searching for specific data, submitting data download requests, and contacting the admin. The evaluation was conducted through online usability testing using the Maze platform with the System Usability Scale (SUS) instrument involving 13 respondents. The evaluation results showed an average SUS score of 78.5, which falls into the “good” category. This confirms that the interface design has met usability standards, is user-friendly, and is capable of supporting user needs in accessing and managing demographic data. Thus, the application of the TCSD method has proven to be effective in producing an interface design that is focused on user tasks and can be the basis for further system development.
Keywords: system usability scale; task centered system design; user interface