Abstract:Abstract: Higher education plays an essential role in improving human resource quality, one of which is through the institution’s ability to monitor and predict student graduation outcomes. This study does not focus on a…
a specific university but utilizes the publicly available Students Performance in Exams dataset from Kaggle, consisting of 1,000 student records containing mathematics, reading, and writing scores, along with demographic attributes such as gender, parental education level, lunch type, and test preparation participation. The data were processed through a feature engineering stage by adding an average score variable as an early indicator of graduation status. A predictive model was developed using the Random Forest Classifier, achieving an accuracy of 94.5%. The final model was integrated into a Streamlit-based web application to provide an accessible tool for academic stakeholders. The results indicate that the proposed model can serve as an effective decision-support tool for early evaluation of students’ likelihood of graduation.
Keywords: prediction; random forest classifier, streamlit, student graduation.
Abstrak: Pendidikan tinggi memegang peran penting dalam peningkatan kualitas sumber daya manusia, salah satunya melalui kemampuan institusi dalam memantau dan memprediksi tingkat kelulusan mahasiswa. Penelitian ini tidak berfokus pada perguruan tinggi tertentu, melainkan menggunakan dataset publik Students Performance in Exams dari Kaggle yang berisi 1.000 data mahasiswa, terdiri atas nilai matematika, membaca, menulis, serta atribut demografis seperti gender, tingkat pendidikan orang tua, jenis makan siang, dan partisipasi kursus persiapan. Data diolah melalui tahap feature engineering dengan menambahkan variabel average score sebagai indikator awal kelulusan. Model prediksi dibangun menggunakan algoritma Random Forest Classifier, yang menghasilkan tingkat akurasi sebesar 94,5%. Model ini kemudian diimplementasikan ke dalam aplikasi web berbasis Streamlit untuk memberikan layanan prediksi yang mudah diakses oleh pihak akademik. Hasil penelitian menunjukkan bahwa model mampu digunakan sebagai alat pendukung keputusan untuk melakukan evaluasi dini terhadap potensi kelulusan mahasiswa.
Kata kunci: kelulusan mahasiswa; prediksi; random forest classifier; streamlit.
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: The Ombudsman of the Republic of Indonesia is an institution tasked with supervising the administration of public services and handling community complaint reports related to allegations of maladministration.…
on. The purpose of this research is to create a decision support system using the Analytic Hierarchy Process (AHP) method, which facilitates the determination of priority handling of community complaint reports at the Ombudsman of the Republic of Indonesia Bengkulu Representation. This decision support system is built on a web-based platform using PHP programming language with a MySQL database that can be accessed offline by the admin of the Ombudsman. With the existence of this priority recommendation, it is expected that work will become more effective and efficient, as resources can be focused on reports that most need attention. Based on the test data used, which consists of 12 Community Complaint Reports from July 2024, it was found that the priority handling recommendations for community complaint reports were derived from 3 reports with the highest final AHP values. The recommended priority handling reports are registration number 0021/LM/VII/2024/BKL with a final AHP value of 2.074, registration number 0020/LM/VII/2024/BKL with a final AHP value of 1.964, and registration number 0018/LM/VII/2024/BKL with a final AHP value of 1.866.
Keywords: decision support system; priority recommendation; public complaint report; AHP Method (analytic hierarchy process method)
Abstract:Abstract: The management of veterinary drug stocks at the Veterinary Clinic Technical Implementation Unit (UPTD) of the North Sumatra Province Plantation and Livestock Service faces obstacles in the form of discrepancies…
between supply and demand, resulting in excess stock and budget waste. Uncertain demand for drugs is a factor that complicates decision-making in stock provision. This study aims to optimize drug stock management using the Mamdani fuzzy logic method, which is capable of handling data uncertainty and modeling information linguistically. Three input variables are used, namely initial stock, demand, and number of visits, with the output being the final stock. The process involves fuzzification, inference based on IF–THEN rules, and defuzzification using the centroid method. The results show that the developed system has a good accuracy level with a MAPE value of 17.52%, which means that this model is effective in providing optimal and efficient drug stock recommendations in a veterinary clinic environment.
Keywords: fuzzy mamdani; optimization; animal drug stock.
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: Uterine disease is a serious threat to women's health, which can affect fertility and quality of life. Delayed diagnosis often results in patients not getting optimal early treatment at the H. Abdul Manan Simatupang…
upang Kisaran Regional General Hospital. This study aims to develop a fuzzy logic-based expert system to diagnose uterine disease based on the symptoms experienced by patients. This system receives symptom data as input, then performs analysis using the fuzzy logic method to determine the level of possibility of a disease. The final results produced are an initial diagnosis and treatment recommendations. System testing shows that this method is able to identify uterine disease with fairly good accuracy, where one case showed the possibility of Endometriosis with a confidence level of 63%. With this system, patients can obtain initial information about their health condition, so they can take more appropriate and faster medical steps.
Keywords: expert system; fuzzy logic; uterine disease.
Abstrak: Penyakit rahim merupakan ancaman serius bagi kesehatan wanita, yang dapat berdampak pada kesuburan dan kualitas hidup. Keterlambatan diagnosis sering kali menyebabkan pasien tidak mendapatkan penanganan dini yang optimal di Rumah Sakit Umum Daerah H. Abdul Manan Simatupang Kisaran. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis logika fuzzy guna mendiagnosis penyakit rahim berdasarkan gejala yang dialami pasien. Sistem ini menerima data gejala sebagai input, kemudian melakukan analisis menggunakan metode logika fuzzy untuk menentukan tingkat kemungkinan suatu penyakit. Hasil akhir yang dihasilkan berupa diagnosis awal dan rekomendasi penanganan. Pengujian sistem menunjukkan bahwa metode ini mampu mengidentifikasi penyakit rahim dengan akurasi yang cukup baik, di mana salah satu kasus menunjukkan kemungkinan penyakit Endometriosis dengan tingkat kepercayaan sebesar 63%. Dengan adanya sistem ini, pasien dapat memperoleh informasi awal mengenai kondisi kesehatannya, sehingga dapat mengambil langkah medis yang lebih tepat dan cepat.
Kata kunci: fuzzy logic; penyakit rahim; sistem pakar.
Abstract:Abstract: Program Indonesia Pintar (PIP) is government assistance program aimed at supporting the education of underprivileged students. However, some PIP fund recipients are misallocated, with aid given to students who…
do not fully meet the eligibility criteria, while those in greater need don’t receive it, including at SDN 014672 Tanjung Alam, Asahan Regency, North Sumatra Province. Based on this issue, a structured system is needed. The purpose of this study is to construct decision support systems for determining PIP fund recipients using Additive Ratio Assessment (ARAS) method. Data was collected using questionnaires, documentation, and observation techniques. Respondents consisted of 8 students from SDN 014672 Tanjung Alam. Criteria include number of dependents, homeownership status, attendance rate, and students final grades. System was developed using CodeIgniter 3 as framework, MySQL as database software, and InnoDB as database engine. ARAS method was applied to rank available alternatives. Based on calculations, first rank was obtained by alternative 6 (Malika Hendra As-Syifa), second rank by alternative 7 (Mutia Indah Sari), and third rank by alternative 8 (Rafa Kavindra). This study is expected to be further developed by applying other DSS methods, performing regular system maintenance, and integrating system with school data to improve accuracy and usability.
Keywords: additive ratio assessment; decision support system; smart indonesia program.
Abstrak: Program Indonesia Pintar (PIP) merupakan bantuan pemerintah untuk mendukung pendidikan siswa kurang mampu. Namun, masih ditemukan penerima anggaran PIP yang kurang tepat sasaran, di mana bantuan diberikan kepada siswa yang kurang memenuhi kriteria, sementara siswa yang lebih membutuhkan tidak menerimanya, termasuk di SDN 014672 Tanjung Alam, Kabupaten Asahan, Provinsi Sumatera Utara. Berdasarkan permasalahan tersebut, dibutuhkan sebuah sistem terstruktur. Tujuan penelitian ini untuk membangun sistem pendukung keputusan penetapan pemeroleh anggaran PIP menggunakan metode Additive Ratio Assessment (ARAS). Data dikumpulkan dengan teknik angket, dokumentasi, dan observasi. Responden adalah 8 siswa SDN 014672 Tanjung Alam. Kriteria meliputi jumlah tanggungan orang tua, status kepemilikan rumah, tingkat kehadiran, dan nilai akhir siswa. Sistem dirancang menggunakan CodeIgniter 3 sebagai framework, MySQL sebagai database software, dan InnoDB sebagai database engine. Perhitungan dengan metode ARAS digunakan untuk merangking alternatif yang ada. Berdasarkan perhitungan yang dilakukan, peringkat pertama diperoleh oleh alternatif 6 yakni Malika Hendra As-Syifa, peringkat kedua diperoleh oleh alternatif 7 yakni Mutia Indah sari, dan peringkat ketiga diperoleh oleh alternatif 8 yakni Rafa Kavindra. Penelitian ini diharapkan dapat dikembangkan lebih lanjut dengan menerapkan metode Sistem Pendukung Keputusan (SPK) lainnya, melakukan pemeliharaan sistem secara berkala, serta mengintegrasikan sistem dengan data sekolah untuk meningkatkan keakuratan dan kemudahan penggunaan.
Kata kunci: additive ratio assessment; program indonesia pintar; sistem pendukung keputusan
Abstract:Abstract: Central Java has significant potential in the plantation sector with various commodities such as pepper, cloves, tobacco, tea, sugarcane, coffee, nutmeg, and patchouli. However, the abundance of commodities does…
s not guarantee that all of them provide maximum benefits. This study aims to recommend the most potential plantation commodities for development. The research utilizes plantation data from Central Java over the past few years, obtained from Satu Data Indonesia, covering land area, production, productivity, and the number of farmers. The evaluation criteria include land area, production, productivity, and the number of farmers. In the decision-making process, a Decision Support System (DSS) approach is applied using the Multi-Attributive Border Approximation Area Comparison (MABAC) method and the Preference Selection Index (PSI). The MABAC method is used to determine rankings, while PSI is used for criteria weighting. The results indicate that sugarcane, tobacco, and robusta coffee are the best commodities, with final scores of 0.419, 0.237, and 0.020, respectively. Therefore, it can be concluded that the most potential commodities for development in Central Java are sugarcane, tobacco, and robusta coffee.
Keywords: central java; MABAC; plantation; PSI
Abstrak: Jawa Tengah memiliki potensi besar di sektor perkebunan dengan berbagai komoditas seperti lada, cengkeh, tembakau, teh, tebu, kopi, pala, dan nilam. Tetapi dengan banyaknya komoditas, tidak memastikan bahwa semua komoditas memberikan manfaat yang maksimal. Penilitian ini bertujuan membuat rekomendasi komoditas perkebunan yang paling potensial untuk dikembangkan. Penelitian ini menggunakan data perkebunan di Jawa Tengah dalam beberapa tahun terakhir yang diperoleh dari Satu Data Indonesia, mencakup luas lahan, produksi, produktivitas, jumlah petani. Kriteria evaluasi yang digunakan meliputi luas lahan, produksi, produktivitas, jumlah petani. Dalam proses pengambilan keputusan, digunakan metode SPK dengan pendekatan (MABAC) serta (PSI). Metode MABAC digunakan untuk menentukan peringkat, sementara PSI digunakan untuk pembobotan kriteria. Hasil yang diperoleh dari penilitian ini yaitu Tebu, Tembakau, Robusta merupakan tanaman terbaik dengan hasil akhir 0,419, 0,237, 0,020. Oleh karena itu, dapat disimpulkan tanaman yang dapat dikembangkan dengan potensial di wilayah Jawa Tengah dengan berbagai macam komoditas yaitu komoditas Tebu, Tembakau, dan Robusta.
Kata kunci: jawa tengah; MABAC; perkebunan ; PSI
Abstract:Abstract: Clustering methods such as K-Means and K-Medoids are often used to analyze data, including student data, due to their efficiency. However, this method has weaknesses, such as sensitivity to selecting cluster centers…
nters (centroids) and cluster results that depend on medoid data. Clustering, an essential technique in data analysis, aims to reveal the natural structure of the data, even in the absence of labeled information. The study, conducted with complete objectivity, compared the performance of two popular clustering methods, K-Means, and K-Medoids, on student data. Three evaluation metrics, namely the Davies-Bouldin Index (DBI), silhouette score, and elbow method, were used to compare clustering and determine the ideal number of clusters for the two algorithms. The data taken in this study are in the form of names, attendance, assignments, formative, midterm exams, final exams, and quality numbers. Based on the existing optimization results, it can be concluded that the K-Means method excels in grouping Student Data. The best results were obtained from the K-Means Algorithm with the Silhouette Coefficient Method with a value of 0.7509 in cluster 2, and the Elbow Method with a value of 1428076.08 in cluster 2, DBI K-Medoids with a value of 0.7413 in cluster 3. So, the best cluster lies in 3 clusters.
Keywords: clustering; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;
Abstrak : Metode clustering seperti K-Means dan K-Medoids sering digunakan untuk menganalisis data, termasuk data siswa, karena efisiensinya. Namun, metode ini memiliki kelemahan, seperti sensitivitas terhadap pemilihan pusat klaster (centroids) dan hasil klaster yang bergantung pada data medoid. Clustering, sebuah teknik penting dalam analisis data, bertujuan untuk mengungkapkan struktur alami dari data, bahkan tanpa adanya informasi berlabel. Penelitian ini, yang dilakukan dengan objektivitas penuh, membandingkan kinerja dua metode clustering populer, yaitu K-Means dan K-Medoids, pada data mahasiswa. Tiga metrik evaluasi, yaitu Davies-Bouldin Index (D.B.I.), silhouette score, dan metode elbow, digunakan untuk membandingkan clustering dan menentukan jumlah cluster yang ideal untuk kedua algoritma tersebut. data yang diambil dalam penelitian ini berupa nama, kehadiran, tugas, formatif, ujian tengah semester, ujian akhir semester, angka mutu. Berdasarkan hasil optimasi yang ada, dapat disimpulkan bahwasannya metode K-Means unggul dalam pengelompokkan Data Mahasiswa. Sehingga di peroleh hasil terbaik dari Algoritma K-Means dengan Metode Silhouette Coefficient dengan nilai 0,7509 di cluster 2, dan Elbow Method dengan nilai 1428076,08 di cluster 2, DBI K-Medoids dengan nilai 0,7413 di cluster 3. Sehingga cluster terbaik terletak pada 3 cluster.
Kata kunci: klasterisasi; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;
Abstract:Abstract: Based on observations and analysis of the current information system and website of the Unilak waste bank, it can be concluded that it is necessary to develop the software and information management system of this…
his waste bank. The application does not yet support the transaction needs at the waste bank. Although there are features, they don’t run as they should. This was also complained about by the director and staff of the Unilak waste bank. Therefore, this study was conducted to design a waste bank transaction management information system and software that will support its business processes, starting from calculating waste prices, managing customer savings to final reports. The research method used is qualitative, using information system analysis techniques and websites that are currently running at the Unilak waste bank. While for the SDLC (software development life cycle) development method, the XP Agile Methodologies cycle is used which is simpler and can be completed in a short time. There are 4 stages, namely Planning (requirements), design, implementation and testing. This study resulted in a customer transaction system design and software development that can help manage customer transaction activities at the Unilak waste bank.
Keywords: costumer; savings; software engineering; waste bank.
Abstrak: Berdasarkan observasi dan analisis terhadap sistem informasi dan website bank sampah Unilak yang berjalan saat ini, dapat disimpulkan bahwa perlu dilakukan pengembangan terhadap perangkat lunak maupun sistem informasinya. Aplikasi belum mendukung kebutuhan transaksi di bank sampah. Meskipun ada fitur-fiturnya, namun tidak berjalan sebagaimana mestinya. Hal ini juga dikeluhkan oleh direktur dan pengelola bank sampah Unilak. Maka penelitian ini dilaksanakan untuk merancang sistem informasi transaksi nasabah bank sampah dan perangkat lunak yang akan mendukung proses bisnisnya, yaitu mulai dari perhitungan harga sampah, pengelolaan tabungan nasabah hingga laporan akhir. Metode penelitian yang digunakan adalah kualitatif, menggunakan teknik analisis sistem informasi dan website yang sedang berjalan di bank sampah Unilak. Sedangkan untuk metode pengembangan SDLC (software development life cycle) menggunakan siklus XP Agile Methodologies yang lebih sederhana dan bisa diselesaikan dalam waktu singkat. Terdapat 4 tahap yaitu planning (requirements), analys, design, dan implementation. Penelitian ini menghasilkan rancangan sistem transaksi nasabah dan pengembangan perangkat lunak yang dapat membantu pengelolaan aktivitas transaksi nasabah di bank sampah Unilak.
Kata kunci: bank sampah; nasabah; rekayasa perangkat lunak; tabungan.