Abstract:Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study…
udy aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs.
Keywords: clustering; data mining; elbow method; k-means; computer course
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: 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: 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.
Abstract:Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming,…
nsuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.
Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVM
Abstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi 90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.
Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM
Abstract:Abstract: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to…
o State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies.
Keywords: arima; LSTM; applicants; prediction; university
Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius.
Kata kunci: arima; LSTM; pendaftar; prediksi; universitas
Abstract:Abstract: Information Technology has impacted various sectors, including education. Learning Management Systems (LMS) are designed to facilitate lecturers and students in accessing academic activities such as online learning.…
ning. This study aims to analyze the effectiveness of Learning Management Systems (LMS) among higher education institutions in Indonesia. This analysis is crucial for assessing the effectiveness of LMS use by universities in Indonesia, enabling investments in LMS to yield optimal results. The study employed the D&M IS Success Model and PLS-SEM to evaluate the relationships between various variables, including system, information, service quality, user satisfaction, and benefits. Simple random sampling was used to collect data from 170 universities in Indonesia. This study employed PLS-SEM to investigate the observed variables, including validity and reliability testing, which involves assessing reliability, convergent validity, and discriminant validity. This current study found that all the hypotheses were accepted with p-values below 0,05. These findings contribute to universities paying attention to aspects of system, information, and service quality in Learning Management Systems (LMS) to improve user satisfaction and create a positive perception of benefits. Therefore, this research yields significant results that contribute to higher education in Indonesia, as well as the advancement of knowledge in management information systems.
Keywords: delone and mclean; information system success; LMS; SEM-PLS.
Abstrak: Teknologi Informasi telah memengaruhi berbagai sektor, termasuk pendidikan. Learning Management System (LMS) dirancang untuk memfasilitasi dosen dan mahasiswa dalam mengakses kegiatan akademik seperti pembelajaran daring. Penelitian ini bertujuan untuk menganalisis efektivitas penggunaan Learning Management System (LMS) di perguruan tinggi di Indonesia. Analisis ini penting untuk menilai sejauh mana efektivitas penggunaan LMS oleh universitas-universitas di Indonesia, sehingga investasi dalam LMS dapat mem-berika n hasil yang optimal.Penelitian ini menggunakan model D&M IS Success Model dan metode PLS-SEM untuk mengevaluasi hubungan antara berbagai variabel, termasuk kuali-tas sistem, informasi, layanan, kepuasan pengguna, dan manfaat. Teknik simple random sampling digunakan untuk mengumpulkan data dari 170 perguruan tinggi di Indonesia. Penelitian ini menggunakan PLS-SEM untuk mengkaji variabel-variabel yang diamati, ter-masuk pengujian validitas dan reliabilitas, yang mencakup penilaian reliabilitas, validitas konvergen, dan validitas diskriminan. Hasil dari penelitian ini menunjukkan bahwa semua hipotesis diterima dengan nilai p di bawah 0,05. Temuan ini mendorong universitas untuk memberikan perhatian pada aspek kualitas sistem, informasi, dan layanan dalam penggunaan LMS guna meningkatkan kepuasan pengguna dan menciptakan persepsi posi-tif terhadap manfaatnya. Oleh karena itu, penelitian ini memberikan hasil yang signifikan bagi perguruan tinggi di Indonesia serta turut berkontribusi dalam pengembangan ilmu di bidang sistem informasi manajemen.
Kata kunci: delone and mclean; kesuksesan sistem informasi; LMS; SEM-PLS
Abstract:Abstract: Air quality in urban areas is becoming an increasingly important issue considering its impact on human health and the environment. The rapid increase in air pollution requires effective methods to predict air quality…
uality in order to take appropriate mitigation measures. This study aims to analyze the use of Neural Network (NN) algorithms in predicting air quality in cities. The method used is the application of the NN model, especially the Multilayer Perceptron (MLP), which is trained using historical air quality data such as dust particle levels (PM10, PM2.5), carbon monoxide (CO) gas, and temperature. The data used in this study came from urban air quality monitoring stations collected over a period of time. The results show that the Neural Network algorithm can provide quite accurate predictions of air quality with a low Mean Absolute Error (MAE) value, showing the effectiveness of the model in predicting f fluctuations in air quality. The conclusion of this study is that Neural Network algorithms, specifically MLPs, are an effective tool for air quality prediction, which can be used as a basis for urban air quality management policies.
Keywords: air quality; neural network; prediction; multilayer perceptron (MLP)
Abstrak: Kualitas udara di perkotaan menjadi isu yang semakin penting mengingat dampaknya terhadap kesehatan manusia dan lingkungan. Peningkatan polusi udara yang pesat memerlukan metode yang efektif untuk memprediksi kualitas udara guna mengambil langkah mitigasi yang tepat. Penelitian ini bertujuan untuk menganalisis penggunaan algoritma Neural Network (NN) dalam memprediksi kualitas udara di perkotaan. Metode yang digunakan adalah penerapan model NN, khususnya Multilayer Perceptron (MLP), yang dilatih menggunakan data kualitas udara historis seperti kadar partikel debu (PM10, PM2.5), gas karbon monoksida (CO), dan suhu. Data yang digunakan dalam penelitian ini berasal dari stasiun pemantauan kualitas udara di perkotaan yang dikumpulkan selama periode waktu tertentu. Hasil penelitian menunjukkan bahwa algoritma Neural Network dapat memberikan prediksi yang cukup akurat terhadap kualitas udara dengan nilai Mean Absolute Error (MAE) yang rendah, menunjukkan efektivitas model dalam memprediksi fluktuasi kualitas udara. Simpulan dari penelitian ini adalah bahwa algoritma Neural Network, khususnya MLP, merupakan alat yang efektif untuk prediksi kualitas udara, yang dapat digunakan sebagai dasar untuk kebijakan pengelolaan kualitas udara di perkotaan
Kata kunci: kualitas udara; neural network; prediksi; multilayer perceptron (MLP)
Abstract:Abstract: The use of e-learning in non-formal education is increasingly important to support the improvement of access to learning, one of which is through the online platform. This study aims to analyze the quality of online…
nline services using WebQual 4.0 and Im-portance Performance Analysis (IPA) methods to evaluate the suitability between user expectations and perceptions. The research method used a quantitative approach by distributing questionnaires to active users, then analyzed using the WebQual Index to measure the overall quality of the system as well as the IPA to determine improvement priorities. The results showed that the quality of SeTARA Online was relatively good with a WebQual Index value of 0.798. However, there is still a gap between user expectations and satisfaction with a negative gap value of -0.238. The IPA analysis identified indicators in Quadrant I as priority improvements, especially in the aspects of service interaction and information presentation. These findings underscore the need for continuous development of features and technical support to optimize the user experience. The conclusion of this study suggests that there should be improvements in priority indicators to increase user satisfaction, as well as strengthen the effectiveness of online learning. Advanced research can expand variables, compare with other platforms, and combine quantitative and qualitative analysis methods for more comprehensive results.
Keywords: e-learning; importance performance analysis; quality of service; online equivalent; webqual 4.0
Abstract:Abstract: Advancements in digital technology have significantly transformed communication and learning. Traditional learning methods have limitations in providing a fast and interactive learning environment, necessitating…
g accessible technology that enhances student and teacher engagement while ensuring convenience. WhatsApp has emerged as a widely used solution due to its accessibility, privacy features, and cross-platform compatibility, offering users a sense of convenience. This study examines the role of Perceived Convenience in the acceptance and use of WhatsApp in secondary education in Medan City using the UTAUT2 Model. A survey was conducted with 439 respondents from 8 secondary schools in Medan and analyzed using SEM-PLS with SmartPLS-4. The results indicate that social influence, hedonic motivation, habit, and perceived convenience positively impact the intention to use WhatsApp. Additionally, facilitating conditions, perceived convenience, and intention to use significantly influence actual usage behavior. However, performance expectancy, effort expectancy, and price value do not affect either intention or behavior in using WhatsApp. Moderating variables such as age, gender, and experience partially moderate the relationships between independent factors and WhatsApp usage intention and behavior. This study contributes by incorporating Perceived Convenience into the UTAUT2 Model and affirming its role in educational technology adoption.
Keywords: perceived convenience; secondary education; UTAUT2; whatsapp
Abstrak: Kemajuan teknologi digital telah membawa perubahan signifikan dalam komunikasi dan pembelajaran. Metode pembelajaran tradisional memiliki keterbatasan dalam menyediakan lingkungan belajar yang cepat dan interaktif, sehingga diperlukan teknologi yang mudah diakses, meningkatkan keterlibatan siswa dan guru, serta nyaman digunakan. WhatsApp menjadi salah satu solusi dan banyak digunakan karena mudah diakses, privasi yang ditawarkan, serta kompatibilitas lintas platform sehingga memberikan kenyamanan yang dapat dirasakan pengguna ketika menggunakannya. Oleh karena itu, Penelitian ini menguji peran Persepsi Kenyamanan terhadap penerimaan dan penggunaan WhatsApp dalam pendidikan menengah di Kota Medan menggunakan Model UTAUT2. Survei dilakukan pada 439 responden dari 8 sekolah menengah di kota Medan, dan dianalisis dengan SEM-PLS menggunakan SmartPLS-4. Hasil penelitian menunjukkan bahwa social influence, hedonic motivation, habit, dan perceived convenience berpengaruh positif signifikan terhadap behavioral intention. Sementara itu, facilitating conditions, perceived convenience, dan behavioral intention berdampak positif pada use behavior. Namun, performance expectancy, effort expectancy, dan price value tidak berpengaruh terhadap niat maupun perilaku penggunaan WhatsApp. Variabel moderasi usia, jenis kelamin, dan pengalaman memoderasi sebagian hubungan antara faktor bebas terhadap niat dan perilaku penggunaan WhatsApp. Penelitian ini berkontribusi dengan menambahkan variabel persepsi kenyamanan (perceived convenience) ke dalam Model UTAUT2 dan menegaskan perannya dalam adopsi teknologi pendidikan.
Kata kunci: pendidikan menengah; persepsi kenyamanan; UTAUT2; whatsapp