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: MBG is a strategic program of the Prabowo-Gibran administration. This program has become a widely discussed issue in the public. To better understand public perception of this program, sentiment analysis is necessary.…
essary. This study aims to compare the performance of algorithms machine learning SVM, RF, And BERT with preprocessing data analyzing public sentiment of the MBG program in media X. The total dataset for this study was 39,858 out of 42,465 successfully crawled tweets. The research methods included data collection, preprocessing data (cleaning, case folding, word normalization, stopword removal and stemming), feature extraction, model training (fine-tuning), handling class imbalance with SMOTE, and evaluation using accuracy, precision, recall, and f1-score. The research results show that without SMOTE, the best performing models are BERT with 89% accuracy, SVM 87%, and RF 78.4%. After SMOTE, the best algorithms were SVM with 92.94%, BERT with 88.3%, and RF with 86.59%. The results confirmed that SVM is the best algorithm if at leastclass imbalance. BERT is the best algorithm before and after SMOTE, because BERT is more effective in capturing the nuances of language on social media, so BERT is the most recommended in MBG sentiment analysis.
Keywords: sentiment analysis; machine learning; SVM, RF, and BERT
Abstrak: MBG merupakan program strategis pemerintahan Prabowo - Gibran. Program ini menjadi isu yang banyak diperbincangkan publik. Untuk mengetahui lebih dalam persepsi masyrakat tentang program ini, perlu dilakukan analisis sentiment. Penelitian ini bertujuan membandingkan kinerja algoritma machine learning SVM, RF, dan BERT dengan preprocessing data menganalisis sentiment public program MBG di media X. Total dataset penelitian ini adalah 39.858 dari 42.465 tweet yang berhasil di crawling. Metode penelitian mencakup pengumpulan data, preprocessing data (cleaning, case folding, normalisasi kata, stopword removal dan stemming), ekstraksi fitur, pelatihan model (fine-tuning), penanganan class imbalance dengan SMOTE, dan evaluasi menggunakan akurasi, presisi, recall, dan f1-score. Hasil peneltian menunjukkan, tanpa SMOTE model dengan kinerja terbaik adalah BERT dengan akurasi 89%, SVM 87%, dan RF 78,4%. Setelah SMOTE algoritma terbaik adalah SVM 92,94%, BERT 88,3% dan RF 86,59%. Hasil penelitian menegaskan bahwa SVM adalah algoritma terbaik jika minimal class imbalance. BERT adalah algoritma terbaik sebelum dan sesudah SMOTE, karena BERT lebih efektif dalam menangkap nuansa bahasa pada media sosial, sehingga BERT paling di rekomendasikan dalam analisis sentimen MBG.
Kata kunci: analisis sentimen; machine learning; SVM, RF, dan BERT
Abstract:Abstract: Stroke is one of the leading causes of death and disability in various parts of the world, including in Indonesia. Along with the development of digital technology, the use of Machine Learning in the health sector…
tor is growing, one of which is in an effort to predict the occurrence of stroke. This study aims to implement the Logistic Regression algorithm in predicting the likelihood of a person having a stroke based on data from the Brain Stroke dataset. The research process includes data preprocessing (missing value handling, normalization, and label encoding), dividing the data into 80% training data and 20% test data, as well as model training. The model was then evaluated using several measures such as accuracy, precision, recall, F1-score, and ROC-AUC, as well as a confusion matrix. The results of the study showed that Logistic Regression was able to provide stroke classification results with an accuracy of 82.4%, precision of 80.1%, recall of 78.6%, F1-score of 79.3%, and a ROC-AUC value of 0.87. Then, the model is integrated into applications that use Streamlit, so it can be used interactively to predict stroke risk in new data. The results of this study show that the combination of Machine Learning and web-based applications has the potential to support efforts to detect early stroke risk.
Keywords: logistic regression; machine learning; prediction; streamlit; stroke.
Abstrak: Stroke adalah salah satu penyebab utama kematian dan kecacatan di berbagai belahan dunia, termasuk di Indonesia. Seiring perkembangan teknologi digital, penggunaan Machine Learning dalam bidang kesehatan semakin berkembang, salah satunya dalam upaya memprediksi terjadinya penyakit stroke. Penelitian ini bertujuan untuk mengimplementasikan algoritma Logistic Regression dalam memprediksi kemungkinan seseorang mengalami stroke berdasarkan data dari dataset Brain Stroke. Proses penelitian meliputi preprocessing data (penanganan missing value, normalisasi, dan label encoding), membagi data menjadi 80% data latih dan 20% data uji, serta pelatihan model. Model kemudian dievaluasi menggunakan beberapa ukuran seperti akurasi, precision, recall, F1-score, dan ROC-AUC, serta confusion matrix. Hasil penelitian menunjukkan bahwa Logistic Regression mampu memberikan hasil klasifikasi penyakit stroke dengan akurasi sebesar 82,4%, precision 80,1%, recall 78,6%, F1-score 79,3%, dan nilai ROC-AUC sebesar 0,87. Kemudian, model tersebut diintegrasikan ke dalam aplikasi yang menggunakan Streamlit, sehingga dapat digunakan secara interaktif untuk memprediksi risiko stroke pada data baru. Hasil penelitian ini menunjukkan bahwa kombinasi Machine Learning dan aplikasi berbasis web berpotensi mendukung upaya deteksi dini risiko stroke.
Kata kunci: logistic regression; machine learning; prediksi; streamlit; stroke.
Abstract:Abstract: YouTube is one of the most popular video streaming platforms, but it has constraints that can cause problems when clients have difficulty finding content according to their wishes. The main objective of this study…
udy is to increase user capacity in viewing content specifically in the field of women's empowerment. By using content-based filtering techniques, the system will analyze user preferences and interests through recommendations for women's empowerment content. The data source is via the YouTube API and is analyzed using PHP programming content-based filtering techniques. The system's recommendations provide a list of women's empowerment content with a user request display. The results of the research evaluation obtained a precision value of 62%, meaning that the recommendations match the topic being searched for, namely women's empowerment. The recall value of 84% indicates that the system has succeeded in finding relations from the database. The f1-score value of 72% indicates that there is a balance between precision and recall, meaning that a system is needed that is not only accurate but also complete. While the cosine value shows a score of 0.7071 approaching the maximum value (1.0). The recommendation of the content-based filtering method produces quite effective women's empowerment content.
Keywords: content-based filtering, recommendations, women Empowerment, youtube
Abstract:Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the…
planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock.
Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index
Abstract:Abstract: Hospitals play an important role in examining the scan results of patient data infected with the Covid 19 virus. However, there are problems when processing the scan results, namely that sometimes errors occur…
in the scan data, causing many failures and delays in sending data to the Health Office. The purpose of this study is to build a Desktop-based decision support system application that can facilitate hospitals in selecting the eligibility of the scan results of Covid 19 patient data. The urgency in examining the scan results of Corona patient data is a very pressing public health issue, because the long-term impact is very significant for patients. Thus, a scientific discipline is needed that can support the decision-making process, namely the Decision Support System using the Preference Selection Index (PSI) method. PSI is a simple and easy calculation method, based on statistical concepts without having to determine attribute weights. The results of this method are clear and firm values based on the level of strength of the rules applied. The results of the research conducted on the PSI process can be concluded that valid Covid 19 patient data is Recap File I with a value of 0.2042 which is declared valid and accepted.
Keywords: covid-19; decision support system; PSI
Abstract:Abstract: This study applies an integrated approach to optimize heart failure classification. The main objective is to address the challenge of class imbalance in medical datasets and to improve the accuracy, sensitivity,…
, and generalization of the classification model. The urgency of this issue is emphasized by statistics showing that cardiovascular diseases cause approximately 17.9 million deaths worldwide each year. Using a quantitative experimental approach, this study analyzes the "Heart Failure Prediction Dataset" from Kaggle, which consists of 918 records. The data were processed through normalization and encoding, followed by the application of SMOTE on the training set to balance class distribution. This step successfully increased model accuracy from 88.41% to 90.22% and minority class recall from 0.82 to 0.88. Furthermore, Bayesian Optimization was employed to refine the hyperparameters of SVM, resulting in a final model with an accuracy of 89.13% that demonstrated better generalization. This integrated approach significantly enhances the stability, sensitivity, and generalization of the model, making it a reliable tool for clinical decision support systems in predicting heart failure.
Keywords: bayesian optimization; heart failure; machine learning; SMOTE; SVM.
Abstrak: Penelitian ini menerapkan pendekatan terintegrasi untuk mengoptimalkan klasifikasi gagal jantung. Tujuan utama studi ini adalah untuk mengatasi tantangan ketidakseimbangan kelas dalam dataset medis dan meningkatkan akurasi, sensitivitas, serta generalisasi model klasifikasi. Urgensi ini ditegaskan oleh statistik yang menunjukkan bahwa penyakit kardiovaskular menyebabkan sekitar 17,9 juta kematian setiap tahun secara global. Menggunakan pendekatan eksperimental kuantitatif, penelitian ini menganalisis "Heart Failure Prediction Dataset" dari Kaggle, yang terdiri dari 918 catatan. Data diproses dengan normalisasi dan encoding, lalu SMOTE diterapkan pada data pelatihan untuk menyeimbangkan distribusi kelas. Langkah ini berhasil meningkatkan akurasi dari 88,41% menjadi 90,22% dan recall kelas minoritas dari 0,82 menjadi 0,88. Selanjutnya, Bayesian Optimization menyempurnakan hyperparameter SVM, menghasilkan model akhir dengan akurasi 89,13% yang menunjukkan generalisasi lebih baik. Pendekatan terintegrasi ini secara signifikan meningkatkan stabilitas, sensitivitas, dan generalisasi model. Hasil penelitian ini menjadikannya alat yang andal untuk sistem pendukung keputusan klinis dalam prediksi gagal jantung.
Kata kunci: bayesian optimization; gagal jantung; machine learning; SMOTE; SVM
Abstract:Abstract: Teacher performance appraisal is a very important aspect in improving the quality of education today, but often occurs during the assessment process of subjectivity constraints and lack of a structured system,…
in this study aims to build a data structure modeling and facilitate the school MAS Islamiyah Hessa Air Genting in the assessment to determine the best teacher transparently and measurably by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The TOPSIS method was chosen because it is able to provide ranking results based on the closeness of alternatives to the ideal solution. In this modeling, assessment criteria data such as pedagogical, professional, personality, social competencies, as well as other indicators such as teacher discipline and achievement are modeled structurally in a relational database. The results show that the designed data structure is able to support the decision-making process efficiently and objectively.
Keywords: data structure; decision support system; teacher assessment; topsis; ranking.
Abstrak: Penilaian kinerja guru merupakan aspek yang sangat penting dalam peningkatan mutu pendidikan saat ini, namun sering terjadi saat proses penilaian kendala subjektivitas dan kurangnya sistem yang terstruktur, dalam penelitian ini bertujuan untuk membangun pemodelan struktur data serta mempermudah pihak sekolah MAS Islamiyah Hessa Air Genting dalam penilaian untuk menentukan guru terbaik secara transparan dan terukur dengan menggunakan algoritma Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Metode TOPSIS dipilih karena mampu memberikan hasil perankingan berdasarkan kedekatan alternatif terhadap solusi ideal. Dalam pemodelan ini, data kriteria penilaian seperti kompetensi pedagogik, profesional, kepribadian, sosial, serta indikator lain seperti kedisiplinan dan prestasi guru dimodelkan secara terstruktur dalam basis data relasional. Hasil penelitian menunjukkan bahwa struktur data yang dirancang mampu mendukung proses pengambilan keputusan secara efisien dan objektif.
Kata kunci: struktur data; topsis; penilaian guru; sistem pendukung keputusan; perangkingan
Abstract:Abstract: The development of information technology encourages businesses to take over digital systems in business operations, even in the sales process. The Point of Sales (POS) system is the leading solution for recording…
ing transactions, managing stock, and creating sales reports efficiently. This study aims to develop a POS application based on a website and make it easier for administrators to manage sales transactions, making them faster and more efficient. This system is made with a structured Agile Development method, requirements, design, development, testing, deployment, and implementation. The framework used is the Laravel framework, with system testing conducted using BlackBox. The test results show that the system is on track and that the efficiency of the transaction and reporting process can be increased. A web-based basis allows users to manage their business more easily in real time because this application is flexible and can be used on various devices.
Keywords: agile model;laravel;point of sales; websites
Abstrak: Pengembangan teknologi informasi mendorong bisnis untuk mengambil alih sistem digital dalam operasi bisnis, bahkan dalam proses penjualan. Sistem Point of Sales (POS) adalah solusi utama untuk merekam transaksi, mengelola stok dan membuat laporan penjualan secara efisien. Tujuan dari penelitian ini adalah untuk mengembangan aplikasi POS berdasarkan situs web dan memudahkan administrator dalam mengelola transaksi penjualan, membuatnya lebih cepat dan lebih efisien. Sistem ini dibuat dengan metode Agile Development yang terstruktur, requirement, design, development, testing, deployment, dan implementation serta kerangka kerja yang digunakan yaitu framework Laravel dengan pengujian sistem menggunakan Blackbox. Hasil pengujian menunjukkan bahwa sistem berada di jalur dan bahwa efisiensi proses transaksi dan pelaporan dapat meningkat. Dengan berbasis web memungkinkan pengguna untuk lebih mudah mengelola bisnisnya secara real time, karena aplikasi ini fleksibel melalui berbagai perangkat.
Kata kunci: model agile;laravel;point of sales;website
Abstract:Abstract: On-time graduation is an important indicator in measuring the success of higher education and reflects the effectiveness of the academic process in higher education. Royal University, especially the Information…
Systems Study Program, still faces challenges in increasing the percentage of students who graduate on time. This study aims to identify factors that influence students' on-time graduation and build a prediction model using the multiple linear regression method. This method was chosen because it is able to analyze the simultaneous influence of several independent numeric variables on one dependent variable, making it suitable for studying the complex relationship between factors that influence student graduation. The independent variables analyzed in this study include GPA, parental income, and student part-time jobs with student graduation as the dependent variable. The results showed that parental income and part-time jobs had a significant positive effect on on-time graduation, while GPA had a negative effect. The model built had an R² value of 0.6153 and a standard error of 4.0653, indicating that the model was quite strong and accurate. These findings recommend Universitas Royal to strengthen the academic monitoring system and support working students, as well as design policies based on students' socio-economic conditions to increase the on-time graduation rate.
Keywords: multiple linear regression; on-time graduation; students.
Abstrak: Kelulusan tepat waktu merupakan indikator penting dalam mengukur keberhasilan pendidikan tinggi serta mencerminkan efektivitas proses akademik di perguruan tinggi. Universitas Royal, khususnya Program Studi Sistem Informasi, masih menghadapi tantangan dalam meningkatkan persentase mahasiswa yang lulus tepat waktu. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi kelulusan tepat waktu mahasiswa serta membangun model prediksi menggunakan metode regresi linear berganda. Metode ini dipilih karena mampu menganalisis pengaruh simultan beberapa variabel independen numerik terhadap satu variabel dependen, sehingga sesuai untuk mengkaji hubungan kompleks antar faktor yang memengaruhi kelulusan mahasiswa. Variabel independen yang dianalisis dalam penelitian ini meliputi IPK, penghasilan orangtua, dan pekerjaan sambilan mahasiswa dengan kelulusan mahasiswa sebagai variabel dependen. Hasil penelitian menunjukkan bahwa penghasilan orangtua dan pekerjaan sambilan berpengaruh positif signifikan terhadap kelulusan tepat waktu, sedangkan IPK justru memiliki pengaruh negatif. Model yang dibangun memiliki nilai R² sebesar 0,6153 dan standar error 4,0653, menandakan model cukup kuat dan akurat. Temuan ini merekomendasikan Universitas Royal untuk memperkuat sistem monitoring akademik dan mendukung mahasiswa yang bekerja, serta merancang kebijakan berbasis kondisi sosial-ekonomi mahasiswa guna meningkatkan angka kelulusan tepat waktu.
Kata kunci: kelulusan tepat waktu; mahasiswa; regresi linear berganda.