Jurnal Matematika, Statistika dan Komputasi

A WORK SCHEDULING APPLICATION OF GENETIC ALGORITHM IN OPTIMAL WORK SCHEDULING OF ED NURSE SERVICE

Mhd Panerangan Hasibuan

Scheduling nurse duty shifts is one of the problems in health organizations that is difficult to solve. The uncontrolled number of patients, the seriousness of the patient's illness, organizational characteristics, absences and personal requests for time off, and the qualifications and specialization of the nurses themselves are several factors why it is difficult to optimize nurse scheduling, including making schedules for each nurse into different working hours. -different in the short term. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. The process of preparing schedules or determining the number of nurses on duty is currently still carried out semi-manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. To solve this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal scheduling of emergency department (IGD) nurse duty. Genetic Algorithm is a search algorithm based on the mechanisms of natural selection and genetics. The genetic algorithm is one of the appropriate algorithms to use in solving complex optimization problems, which are difficult to do with conventional methods. The Genetic Algorithm is good enough to be used in optimizing shift scheduling for the Nursing Service in a Hospital, because this genetic algorithm can solve multi-criteria and multi-objective problems to solve problems modeled using biological and evolutionary processes. So the genetic algorithm concept can be applied in optimizing the shift schedule for the Nursing Service. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions, in other words being eliminated by mutation because they were unable to compete with several other strong individuals.

ANALISIS PENGARUH FAKTOR-FAKTOR PSIKOLOGIS TERHADAP MINAT MAHASISWA DALAM MENGIKUTI PROGRAM MAGANG KAMPUS MERDEKA MENGGUNAKAN METODE STRUCTURAL EQUATION MODELING PARTIAL LEAST SQUARE

Arlene Henny Hiariey, Yayan Ode

Internship is part of a job training system that is held in an integrated manner between training at training institutions by working directly under the guidance and supervision of instructors or more experienced workers / laborers, in the process of producing goods and / or services in the company, in order to master certain skills or expertise. The internship program is part of the Merdeka Belajar Kampus Merdeka (MBKM) program, which provides opportunities for students from various universities in Indonesia in coordination with the ministry of education, culture, research, and technology (Kemendikbudristek) to learn in the world of work. In this study, the object of research was a student of the Statistics Study Program at Pattimura University. The Statistics Study Program is one of the study programs in the Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Pattimura University, Ambon. The purpose of this study is to model SEM-PLS based on cases from the results of questionnaire analysis, determine indicator variables that are feasible to be used as an influence on latent variables based on the SEM-PLS method, and identify the influence of exogenous latent variables, namely Motivation, Value Perception of Internship Program. SEM is one of the Multivariate data analysis methods which is a statistical analysis method to analyze several variables simultaneously. Based on the results and discussion in this study is modeling the influence of motivation, Perception of Internship Program Value, Perception of Self-Ability, and Perception of Specific Support for Student Interest resulting in the following structural equations:. These results are valid, reliable, and have been evaluated both through reflective and formative measurement processes.

Analisis Jalur Pengaruh Faktor-Faktor Ekonomi dan Sosial Terhadap Indeks Pembangunan Manusia di Sulawesi Selatan Tahun 2022

Anni Ivoni Parapa, Clarisa Eudia Chesynanda, Siswanto Siswanto, Anisa Kalondeng

Indeks Pembangunan Manusia (IPM) menjadi indikator penilaian perkembangan sosial ekonomi di suatu daerah. Setiap wilayah berupaya meningkatkan IPM dengan memperhatikan faktor-faktor yang memengaruhi IPM di wilayah tersebut. Penelitian ini memiliki tujuan untuk mengidentifikasi pengaruh langsung dan pengaruh tidak langsung dari faktor-faktor ekonomi dan sosial, seperti Umur Harapan Hidup (UHH), PDRB per kapita ADHB, Tingkat Partisipasi Angkatan Kerja (TPAK) melalui Rata-rata Lama Sekolah (RLS) terhadap IPM di Sulawesi Selatan tahun 2022. Data yang digunakan dalam penelitian ini merupakan data sekunder yang bersumber dari BPS Provinsi Sulawesi Selatan tahun 2022. Metode yang diterapkan dalam penelitian ini yaitu analisis jalur yang mengkaji hubungan antar variabel, baik pengaruh langsung maupun pengaruh tidak langsung. Hasil penelitian menunjukkan pada persamaan sub struktur 1, UHH dan PDRB per kapita ADHB memiliki pengaruh secara langsung terhadap RLS, sedangkan TPAK tidak berpengaruh secara langsung terhadap RLS. Besar pengaruh variabel pada sub struktur 1 sebesar 53%. Pada persamaan sub struktur 2, UHH, PDRB per kapita ADHB, TPAK, dan RLS berpengaruh signifikan secara langsung terhadap IPM. Selain itu, UHH dan PDRB per kapita ADHB memiliki pengaruh tidak langsung melalui RLS terhadap IPM. Besar pengaruh variabel pada sub struktur 2 sebesar 93,5%. Jadi, variabel yang memiliki pengaruh langsung dan tidak langsung terhadap IPM melalui RLS adalah UHH dan PDRB per kapita ADHB.

Pemodelan Indeks Harga Saham Gabungan Berdasarkan Model Regresi Nonparametrik Penalized Spline

Dhita Hartanti Octavia, Asma Auliarani, Siswanto Siswanto, Anisa Kalondeng

The Composite Stock Price Index (IHSG) is a critical indicator in the Indonesian capital market and plays a central role as one of the key instruments that influences the dynamics of a country's economy. IHSG modeling can provide substantial contributions to interested parties in the capital market and encourage investment decision making. Therefore, it is important to produce estimates that are precise and responsive to IHSG data. The IHSG data used is in the period January 2020 to December 2022 and tends to fluctuate, so spline regression analysis with penalized spline estimation is applied which is effective for overcoming limited assumptions in the relationship between variables. From the analysis results, the optimum values based on the minimum Generalized Cross Validation (GCV) for each variable are 0.278, 0.904, 0.751, and 0.665, respectively. It was also known that inflation, exchange rates, interest rates, and IDJ together had an influence of 92.1% on the IHSG movement with inflation rates having varying impacts, exchange rates with a stronger negative effect at certain levels, interest rates with the opposite effect depending on the level, and IDJ with a positive effect on the IHSG. The significant variability of this impact shows that these variables make an important contribution or in other words, IHSG fluctuations can be explained by variations in the values of inflation, exchange rates, interest rates and IDJ.

r-Chromatic Number On r-Dynamic Vertex Coloring of Comb Graph

Heryati Nur Fatimah Sari, Budi Nurwahyu, Jusmawati Massalesse

Let be a graph with vertex set and edge set . An r-dynamic vertex coloring of a graph is a assigning colors to the vertices of such that for every vertex receives at least colors in its neighbors. The minimum color used in r-dynamic vertex coloring of graph is called the r-dynamic chromatic number denoted as . In this research we well determine the coloring pattern and the r-dynamic chromatic number of the comb graph , central graph of comb graph , middle graph of comb graph , line graph of comb graph , sub-division graf of comb graph , and para-line graph of comb graph .

Optimizing Drinking Water Distribution Routes Using the Clarke & Wright Algorithm (AISUKE Drinking Water Depot)

Sri Artha Dwi Anjarwati, Depriwana Rahmi

Aisuke is a drinking water supply company in Pekanbaru City. The research problem is determining the distribution route for gallon drinking water products from distributors to customers. There are often delays in the delivery of gallons of product, and some customers are not served according to the delivery schedule. This problem will be solved by using the Clarke and Wright savings heuristic in the hope of minimizing the delivery time for gallon drinking water products to customers. Clarke and Wright's parsimonious heuristic works by selecting customer locations during the delivery process based on the closest distance, resulting in optimal delivery routes that are effective and efficient.

C Comparison Predictions of the Demam Berdarah Dengue (DBD) using Model Exponential Smoothing: Pegel’s Classification and ChatGPT

Wiwik Wiyanti

The evolution of AI since the Covid-19 pandemic has developed very rapidly. Until 2023, AI is claimed to be a threat to several professional jobs, especially data analysts and scientists The purpose of this research is to check the effectiveness chat-GPT to predict about demam berdarah dengue (DBD) case. Method of the analyzing the data in this research is Mixed method. Quantitative method using exponential smoothing: pegel’s classification and qualitative method using GPT-3. The aim of this research is to check whether ChatGPT can predict the demam berdarah dengue (DBD) data time series. The prediction result are check it by exponential smoothing: pegel’s classification method. The benefit of this research is it can be used to reference how far the evolution of AI can be threaten the profession of data analyst or data scientist. The result of this study conclude that the ChatGPT (GPT-3) can’t predict DBD’d data correctly

Keywords: prediction, ChatGPT, pegel’s, dbd, forecast

Ordinal Logistic Regression Analysis of Factors Affecting Mathematics Literacy Achievement of Junior and Senior High School in West Manggarai Regency

Patrisius Afrisno Udil, Dadan Dasari, Elah Nurlaelah

This study aims to analyze the significant factors that affect mathematics literacy achievement, especially in junior and senior high schools in West Manggarai Regency. It begins by presenting descriptive information regarding mathematics literacy achievement at junior and senior high schools in West Manggarai Regency, especially in relation to other factors measured in the national assessment. This study is a quantitative study. The data analyzed were secondary data obtained from the Education, Youth and Sports Office of West Manggarai Regency in the form of Education Report. The analysis was carried out descriptively first, then analyzed with ordinal logistic regression. The results showed that the achievement of mathematics literacy at junior and senior high schools in West Manggarai Regency is still not optimal, both in general and in terms of each factor. The results of this study also show that the factors that significantly influence the achievement of mathematical literacy at junior and senior high schools in West Manggarai Regency include the level of the education unit, increased of learning quality score, increased of instructional leadership score, and increased of the algebraic content score.

KLASIFIKASI KETINGGIAN MUKA AIR UNTUK MENDETEKSI STATUS BENCANA BANJIR MENGGUNAKAN KECERDASAN BUATAN

Jiwa Akbar

Flooding occurs when the water surface elevation exceeds the normal level, resulting in river water overflowing and creating inundation in low-lying areas. The importance of early warning for potential floods is significant in reducing losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The results of the study indicate that the SVM algorithm outperforms the KNN algorithm. In the scenarios conducted, with the SVM algorithm using the parameter C ranging from 1 to 10 and the rbf kernel, it achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.

K-Means Classification Analysis of Concept Understanding and Self-efficacy: Exploring the Relationship in an Educational Context

Magdalena Wangge

Concept understanding and self-efficacy are two important aspects of mathematics learning that are interrelated. However, there is still debate about the relationship between these two aspects in the context of mathematics learning. Therefore, this study was conducted to analyze the classification of concept understanding and self-efficacy using K-means clustering with a sample of grade VIII students from three selected schools in Wolowaru District and Kelimutu District, Ende Regency, NTT. Data were collected through concept understanding test and self-efficacy questionnaire. The results showed that students' concept understanding and self-efficacy belonged to the medium and low classes. However, there was a practically insignificant correlation between the two aspects. The implication is the importance of developing learning strategies that can improve students' concept understanding and self-efficacy in the context of mathematics education.