Abstract:Abstract: The materials sector is one of the stock markets sectors that attracts investors due to the high level of construction activity in Indonesia, which supports long-term growth. Stock price movements are influenced…
d by various factors, requiring investors to determine the appropriate timing for buying, selling, or holding stocks. Therefore, this study aims to predict stock prices in the materials sector using a combination of CNN–BiLSTM algorithms. The research data were obtained from Yahoo Finance and processed through min–max normalization, data splitting, sliding window, model implementation, and evaluation stages. Testing was conducted on INTP and SMGR stocks with data split scenarios ranging from 60:40 to 90:10. The results show that CNN–BiLSTM performs best with a 90:10 data split, with minimum MSE and MAPE values of 0.000153 and 2.471% for INTP, and 0.000199 and 2.208% for SMGR, respectively. These findings indicate that increasing the proportion of training data improves the model's ability to learn historical patterns and produce more stable predictions.
Keywords: CNN-BILSTM; materials sector; stock
Abstrak: Sektor materials merupakan salah satu sektor saham yang diminati investor karena tingginya aktivitas pembangunan di Indonesia yang mendorong pertumbuhan jangka panjang. Pergerakan harga saham dipengaruhi oleh berbagai faktor sehingga investor perlu menentukan waktu transaksi yang tepat. Oleh karena itu, penelitian ini bertujuan memprediksi harga saham sektor materials menggunakan kombinasi algoritma CNN–BiLSTM. Data penelitian diperoleh dari Yahoo Finance dan diproses melalui tahapan normalisasi min–max, pembagian data, sliding window, implementasi model, serta evaluasi. Pengujian dilakukan pada saham INTP dan SMGR dengan skenario pembagian data 60:40 hingga 90:10. Hasil menunjukkan bahwa CNN–BiLSTM menghasilkan performa terbaik pada pembagian data 90:10, dengan nilai MSE dan MAPE minimum masing-masing sebesar 0.000153 dan 2.471% untuk INTP, serta 0.000199 dan 2.208% untuk SMGR. Temuan ini mengindikasikan bahwa peningkatan porsi data latih meningkatkan kemampuan model dalam mempelajari pola historis dan menghasilkan prediksi yang lebih stabil.
Kata kunci: CNN-BILSTM; saham; sektor materials
Abstract:This study aims to apply the Analytic Network Process (ANP) method as a decision support tool in determining the eligibility of education grant recipients in North Sumatra Province. The background of this research arises…
from the large number of grant applicants compared to the available budget, as well as the absence of clear and objective evaluation standards. The ANP method was chosen because it allows the interdependence between assessment criteria such as institutional feasibility, performance and achievement, social and educational impact, and accountability and transparency to be analyzed comprehensively. Data were obtained through interviews, documentation, and observation at the North Sumatra Provincial Education Office. The results of the ANP model show that the criterion with the highest weight is accountability and transparency (0.44), followed by social and educational impact (0.31). Among the three alternatives, community-based education foundations (A2) obtained the highest total weight (0.30), indicating that they are the most eligible recipients of education grants. The implementation of the ANP-based decision support system produces valid and consistent ranking results (CR < 0.1), enabling faster, fairer, and more transparent decision-making. Therefore, the ANP method contributes significantly to improving governance, objectivity, and accountability in the distribution of education grants in North Sumatra Province.
Abstract:Abstract: Understanding students’ emotional conditions is important for evaluating engagement and learning atmosphere in classroom environments. However, conventional evaluation methods are often subjective and difficult…
lt to apply in real time. Therefore, this study proposes a real-time multi-face emotion detection system designed for classroom learning environments. The system integrates a CNN-based Tiny Face Detector for multi-scale face localization with a convolutional neural network to classify seven facial emotions: angry, disgust, fear, happy, sad, surprise, and neutral. Experimental evaluation was conducted using classroom video data under varying lighting conditions, face orientations, partial occlusions, and different numbers of detected faces per frame. The proposed system achieves stable real-time performance with processing speeds ranging from 10–20 FPS, depending on face density. The results show higher recognition performance for expressive emotions, while subtle emotions remain more challenging. Overall classification accuracy reaches above 80% when emotion predictions are aggregated across multiple faces and time windows. These results indicate that the proposed system is suitable for objective analysis of emotional dynamics in classroom environments and supports the deployment of lightweight emotion-aware monitoring systems for educational applications.
Keywords: classroom monitoring; convolutional neural network; facial emotion recognition; multi-face detection; tiny face detector.
Abstrak: Pemahaman terhadap kondisi emosional mahasiswa penting untuk mengevaluasi keterlibatan dan suasana pembelajaran di kelas. Namun, metode evaluasi konvensional umumnya bersifat subjektif dan sulit diterapkan secara real-time. Oleh karena itu, penelitian ini mengusulkan sistem deteksi emosi multi-wajah secara real-time yang dirancang untuk lingkungan pembelajaran di kelas. Sistem mengintegrasikan Tiny Face Detector berbasis CNN untuk pelokalan wajah multi-skala dengan jaringan saraf konvolusional untuk mengklasifikasikan tujuh emosi wajah, yaitu marah, jijik, takut, senang, sedih, terkejut, dan netral. Evaluasi eksperimen dilakukan menggunakan data video kelas dengan variasi kondisi pencahayaan, orientasi wajah, oklusi parsial, serta jumlah wajah yang berbeda dalam satu frame. Sistem menunjukkan kinerja real-time yang stabil dengan kecepatan pemrosesan antara 10–20 FPS, bergantung pada kepadatan wajah. Hasil pengujian menunjukkan kinerja yang lebih baik pada emosi ekspresif, sementara emosi dengan ciri halus lebih menantang untuk dikenali. Akurasi klasifikasi keseluruhan mencapai di atas 80% ketika hasil emosi diagregasi berdasarkan banyak wajah dan interval waktu. Hasil ini menunjukkan bahwa sistem yang diusulkan berpotensi digunakan untuk analisis objektif dinamika emosi di kelas serta mendukung pemantauan lingkungan pembelajaran berbasis kecerdasan buatan.
Kata kunci: pengenalan emosi wajah; deteksi multi-wajah; Tiny Face Detector; jaringan saraf konvolusional; pemantauan kelas.
Abstract:Abstract: The implementation of dress code regulations in university environments is generally still carried out conventionally, requiring significant time and effort and potentially leading to subjective assessments. This…
is study develops an automatic student dress code compliance detection system using computer vision based on the YOLOv8 model. The dataset consists of 1,800 annotated images divided into eight clothing categories, split into 78% training (1,404 images), 14% validation (254 images), and 8% testing (143 images). All images underwent preprocessing and data augmentation before training the YOLOv8 model with an input size of 640×640 pixels for 50 epochs. During testing, the YOLOv8 model achieved an overall performance of Precision 0.844, Recall 0.773, F1-Score 0.802, and mAP@0.5 0.841, and was able to detect clothing objects with good accuracy and stable performance under various image conditions. The system was integrated with a Flask-based backend and a web-based frontend to enable real time detection and compliance classification, with a response time of less than 2 seconds, supporting automatic and consistent identification of student dress code compliance as “Compliant” or “Violation.”
Keywords: compliance detection; computer vision; dress code regulations; real time detection; YOLOv8.
Abstrak: Penerapan aturan berpakaian di lingkungan kampus umumnya masih dilakukan secara konvensional sehingga membutuhkan waktu dan tenaga yang relatif besar serta berpotensi menimbulkan subjektivitas penilaian. Penelitian ini bertujuan mengembangkan sistem pendeteksi kepatuhan berpakaian mahasiswa secara otomatis berbasis visi komputer menggunakan model YOLOv8. Dataset yang digunakan terdiri dari 1.800 citra beranotasi yang terbagi ke dalam 8 kategori pakaian, dengan pembagian data sebesar 78% data latih (1.404 citra), 14% data validasi (254 citra) dan 8% data uji (143 citra). Seluruh citra diproses melalui tahapan pre-processing dan data augmentation, kemudian digunakan untuk melatih model YOLOv8 dengan ukuran input 640×640 piksel selama 50 epoch. Pada tahap pengujian, model mencapai performa keseluruhan dengan Precision 0.844, Recall 0.773, F1-Score 0.802, dan mAP@0.5 0.841, serta mampu mendeteksi objek pakaian dengan akurasi baik dan performa stabil pada berbagai kondisi citra. Sistem kemudian diintegrasikan dengan backend berbasis Flask dan frontend web untuk mendukung proses deteksi waktu nyata dan klasifikasi kepatuhan, dengan waktu respons sistem kurang dari 2 detik, sehingga mampu mengidentifikasi status kepatuhan berpakaian mahasiswa ke dalam kategori “Aman” dan “Melanggar Aturan” secara otomatis dan konsisten.
Kata kunci: aturan berpakaian; deteksi waktu nyata; pendeteksi kepatuhan; visi komputer; YOLOv8.
Abstract:Abstract: The capital market plays an important role in describing the economic conditions of a country, and the IHSG is used as the main indicator to observe the movement of all stocks on the Indonesia Stock Exchange. Because…
ecause stock data is volatile and non-linear, the forecasting process becomes challenging, requiring methods that can capture historical patterns more accurately. This study aims to predict IHSG movements using the Long Short-Term Memory (LSTM) model to generate stable short-term predictions. Historical IHSG data was used to train the model, and accuracy was evaluated using Mean Squared Error (MSE). The results show that the model obtained an MSE 6784.0207, RMSE 82.3652 and MAPE 0.88%, indicating a relatively low prediction error rate. The visualization shows that the model's predictions are very close to the actual data, and the 60-day forecasting results show a potential increase in the IHSG of 1.05%. Thus, the LSTM model is capable of providing fairly accurate IHSG predictions and can be a useful tool for investors in analyzing short-term market movements.
Keywords: forecasting; JCI; long short term memory
Abstrak: Pasar modal memiliki peran penting dalam menggambarkan kondisi ekonomi suatu negara, dan IHSG digunakan sebagai indikator utama untuk melihat pergerakan seluruh saham di Bursa Efek Indonesia. Karena data saham bersifat fluktuatif dan tidak linear, proses peramalan menjadi tantangan, sehingga dibutuhkan metode yang mampu menangkap pola historis secara lebih akurat. Penelitian ini bertujuan memprediksi pergerakan IHSG menggunakan model Long Short-Term Memory (LSTM) untuk menghasilkan prediksi jangka pendek yang stabil. Data historis IHSG digunakan untuk melatih model, kemudian akurasi dievaluasi menggunakan Mean Squared Error (MSE). Hasil penelitian menunjukkan bahwa model memperoleh nilai MSE 6784.0207, RMSE 82.3652 dan MAPE 0.88% yang menandakan tingkat kesalahan prediksi relatif rendah. Visualisasi menunjukkan bahwa prediksi model sangat mendekati data aktual, dan hasil forecasting 60 hari ke depan memperlihatkan potensi kenaikan IHSG sebesar 1,05%. Dengan demikian, model LSTM mampu memberikan prediksi IHSG yang cukup akurat dan dapat menjadi alat bantu bagi investor dalam menganalisis pergerakan pasar jangka pendek.
Kata kunci: peramalan; JCI; memori jangka pendek
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