Abstract:Abstract: Heart disease is one of the leading causes of death worldwide, making early detection and accurate diagnosis crucial for reducing mortality rates and improving patient outcomes. This study aims to evaluate the…
effectiveness of four machine learning algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—in predicting heart disease, with a focus on enhancing model performance using Linear Discriminant Analysis (LDA) for feature reduction. Among the models, SVM achieved the highest accuracy at 84.24%, followed by Logistic Regression at 83.70%. Although Random Forest and KNN showed lower accuracies, all models benefited from LDA's dimensionality reduction. This study suggests that SVM, combined with LDA, offers an optimal solution for early and accurate heart disease prediction in the healthcare industry.
Keywords: feature reduction; heart disease; linear discriminant analysis (LDA); machine learning; SVM
Abstrak: Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, sehingga deteksi dini dan diagnosis yang akurat sangat penting untuk menurunkan angka kematian dan meningkatkan hasil pengobatan pasien. Penelitian ini bertujuan untuk mengevaluasi efektivitas empat algoritma pembelajaran mesin—Regresi Logistik, Random Forest, Support Vector Machine (SVM), dan K-Nearest Neighbors (KNN)—dalam memprediksi penyakit jantung, dengan fokus pada peningkatan kinerja model menggunakan Analisis Diskriminan Linear (LDA) untuk reduksi fitur. Di antara model yang diuji, SVM mencapai akurasi tertinggi sebesar 84,24%, diikuti oleh Regresi Logistik dengan 83,70%. Meskipun Random Forest dan KNN menunjukkan akurasi yang lebih rendah, semua model memperoleh manfaat dari reduksi dimensi yang diberikan oleh LDA. Studi ini menunjukkan bahwa SVM yang dikombinasikan dengan LDA merupakan solusi optimal untuk prediksi penyakit jantung secara dini dan akurat dalam industri kesehatan.
Kata kunci: linear discriminant analysis (LDA); machine learning; penyakit jantung; reduksi fitur; SVM.
Abstract:Abstract: Gross Regional Domestic Product (GRDP) is one of the most important socio-economic indicators. In order to gain a more comprehensive understanding of the current economic situation and regional differences, estimating…
imating GRDP using integration of satellite imagery and official statistics data can provide valuable information. This research estimates the GRDP value in 2022 by using data in 2019 to 2021 related to two aspects, agriculture and non-agriculture. Soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), and land cover (LC) used as agriculture aspect, while nighttime light (NTL), human settlement index (HSI), land area, and population per regency/city used as non-agriculture aspect. GRDP estimation are produced with machine learning approach using support vector machine (SVM) and random forest (RF) method. Correlation test on each variable shows only land area that does not have a significant correlation with GRDP. RF model then chosen as the best model with RMSE, MSE, MAE, and R2 value of 0.2549; 0.5049; 0.7727; and 0.2543, respectively. The estimated values acquired in several regencies/cities have rather near, some even very close to the official statistics values.
Keywords: GRDP; satellite imagery; machine learning; random forest; support vector machine
Abstrak: Produk Domestik Regional Bruto (PDRB) merupakan salah satu indikator sosio-ekonomi yang penting. Penghitungan nilai PDRB dengan pendekatan yang melibatkan kombinasi data citra satelit dan statistik resmi dapat memberikan informasi serta pemahaman yang lebih komprehensif. Penelitian ini melakukan estimasi nilai PDRB pada tahun 2022 menggunakan data tahun 2019 hingga 2021 dengan melibatkan dua aspek, agrikultur dan non-agrikultur. Data soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), dan tutupan lahan (land cover/LC) digunakan sebagai aspek agrikultur, sementara data citra cahaya malam (NTL), human settlement indeks (HSI), luas wilayah kabupaten/kota, dan jumlah populasi per kabupaten/kota digunakan sebagai aspek non-agrikultur. Estimasi PDRB dihasilkan dengan menggunakan pendekatan machine learning berupa support vector machine (SVM) dan random forest (RF). Pengecekan korelasi antarvariabel menunjukkan bahwa hanya variabel luas wilayah tidak berpengaruh signifikan terhadap nilai PDRB. Model random forest kemudian dipilih sebagai model terbaik dengan nilai evaluasi RMSE, MSE, MAE, dan berturut-turut sebesar 0.2549, 0.5049, 0.7727, dan 0.2543. Nilai estimasi yang diperoleh di beberapa kabupaten/kota cukup mendekati, bahkan ada yang sangat dekat dengan nilai statistik resmi.
Kata kunci: PDRB; citra satelit; machine learning; random forest; support vector machine
Abstract:Abstract: A research institute explains that with 83.7 million people using the Internet, Indonesia is among the top 20 internet users globally. Various individual or group activities require an internet network, one of…
which is playing games, for developments in the gaming sector, especially the MOBA (Massive Online Battle Arena) genre game, is being hotly discussed. There are various kinds of MOBA genre games, one of which is the Mobile Legends game. Many E-Sport Mobile Legends teams, especially in Asia, make this phenomenon a business space to generate large profits. In this study, the researcher recommends a good machine learning algorithm to predict the outcome of Mobile Legends matches. Of the 600 match history data analyzed, this study recommends the Artificial Neural Network (ANN) and Random Forest (RF) algorithms as the right algorithms to predict the outcome of the match. Prediction results from each algorithm can reach 82% and 80% accuracy. These findings can help the E-sports analysis team build their match strategy.
Keywords: artificial neural networ; machine learning; mobile legend; prediction; random forest
Abstrak: Sebuah lembaga penelitian menjelaskan bahwa dengan 83,7 juta penduduk yang menggunakan Internet, Indonesia termasuk di dalam 20 besar pengguna internet secara global. Berbagai aktivitas individu atau kelompok membutuhkan jaringan internet, salah satunya adalah bermain game, untuk perkembangan pada sektor game khususnya game bergenre MOBA (Massive Online Battle Arena) sedang hangat diperbincangkan. Ada berbagai macam game bergenre MOBA, salah satunya game Mobile Legends. Banyak tim E-Sport Mobile Legends khususnya di asia menjadikan fenomena ini sebagai ruang bisnis untuk menghasilkan keuntungnya yang besar. Dalam penelitian ini, peneliti merekomendasikan algoritma pembelajaran mesin yang baik untuk memprediksi hasil pertandingan Mobile Legends. Dari 600 data riwayat pertandingan yang dianalisis, penelitian ini merekomendasikan algoritma Artificial Neural Network (ANN) dan Random Forest (RF) sebagai algoritma yang tepat untuk memprediksi hasil pertandingan. Hasil prediksi dari masing-masing algoritma dapat mencapai 82% dan akurasi 80%. Temuan ini dapat membantu tim analisis E-sports membangun strategi pertandingan mereka.
Kata kunci: artificial neural network; machine learning; mobile legend; prediksi; random forest
Abstract:Abstract: Distribution is an important the business sector, the agricultural sector for distributing seeds to ensure the location of customers selling seeds. Problems that are often encountered seed distribution process…
are the efficiency of the time and distance distribution. Re search will build software entering initial location data and several dynamically added consumer agents. The distance parameter uses latitude-longitude integrated on google maps and detects varying store locations, the generation of chromosomes or the best distribution path with the minimum distance route. The heuristic approach using the Genetic Algorithm imitates the concept of biological evolution of random exchange structure series. This study is to distribute 3 types of seeds with a choice of weights that have been divided into 3 areas located on the map of Indonesia using land routes. The results of the test of the population of the average fitness value tend to remain from the previous value of 1-10 the fitness value and the optimum iteration with 9-12 with an average fitness value of 44.2. Optimal results are obtained when Mr is higher than the Cr values. Thus, the Genetic Algorithm can be used for TSP seed distribution paths. 1:2 fitness evaluation compared with the usual estimates used .
Keywords: Genetic Algorithm; Route Optimization; Seed Distribution; TSP
Abstrak: Distribusi menjadi hal penting berwirausaha, salah satunya pada bidang pertanian untuk pendistribusian benih sampai lokasi tujuan. Permasalahan sering ditemui dalam proses pendistribusian adalah efektifan, efisiensi waktu dan jarak tempuh. Sehingga penelitian akan membangun perangkat lunak dengan memasukan data titik lokasi awal dan beberapa lokasi tujuan agen konsumen ditambahkan secara dinamis. Parameter jarak menggunakan latitude-longitude terintegrasi pada google maps yang mendeteksi keberadaan lokasi, selanjutnya diketahui generasi kromosom atau jalur distribusi terbaik dengan rute minimum. Pendekatan Heuristic menggunakan Algoritma Genetika meniru konsep evolusi biologis deretan struktur pertukaran informasi secara acak. Tujuan dalam penelitian ini dapat mendistribusikan jenis benih dengan pilihan bobot yang telah terbagi dalam wilayah lokasi. Satu wilayah lokasi terdapat beberapa lokasi toko ditambahkan secara dinamis, dengan proses yang sudah ditentukan titik awal keberangkatan. Penelitian ini menekankan pada proses penentuan rute lokasi saja. Hasil pengujian jumlah populasi rata-rata nilai fitness cenderung bersifat tetap dari nilai sebelumnya selisih 1-10 nilai fitness dan iterasi optimum dengan 9-12 dengan rata-rata nilai fitness 44,2. Hasil optimum didapatkan ketika Mutation rate (Mr) lebih tinggi dibanding nilai Crossover rate (Cr). Maka, Algoritma Genetika bisa digunakan untuk TSP jalur distribusi benih pengujian menghasilkan evaluasi fitnes 1:2 untuk Algoritma Genetika dibandingkan dengan estimasi jarak biasa digunakan.
Kata kunci: Algoritma Genetika; Distribusi Benih; Optimalisasi Rute; TSP
Abstract:Abstract: Student objectivity in filling out E-EDOM is very necessary, the results of data analysis become the basis for the leadership in making decisions for learning strategies. Previous research has proven that the E-EDOM…
-EDOM filling done by students is not objective. Efforts should be made so that students can fill it in seriously. This study aims to determine strategies that can be applied so that students can fill out the E-EDOM very objectively. Starting with analyzing and collecting data through a questionnaire. The Delon and Mclean model is used as a basis for creating questions in the questionnaire and processing the questionnaire data. The data collection technique used a simple random sampling method, with a population of 121 students from Amik Riau STMIK who were active in the 2019-2020 school year. Before processing, the data is tested for validity and reliability. After testing the validity and reliability, then testing the hypothesis to determine the relationship between variables in the Delon and Mc Lean success model. The results showed that each variable showed the effect of a positive relationship that was fully accepted with a value of 0.000. This means that all the variables used in this study are related to one another. Even so, it is necessary to implement several strategies that must be done in an effort to fill out E-EDOM which is very objective by students.
Keywords: e-edom, delon and mclean models, objectivity, students.
Abstrak: Objektifitas mahasiswa dalam mengisi E-EDOM sangat diperlukan, karena hasilnya akan dijadikan dasar oleh pimpinan dalam mengambil keputusan untuk masalah pembelajaran. Berdasarkan penelitian sebelumnya pengisian E-EDOM yang dilakukan mahasiswa masih banyak yang belum mengisinya dengan sangat objektif. Perlu dilakukan upaya agar mahasiswa dapat mengisinya dengan sunggun-sungguh. Penelitian ini bertujuan untuk menentukan strategi yang dapat diterapkan supaya mahasiswa dapat mengisi E-EDOM dengan sangat objektif. Diawali dengan melakukan analisa dan pengumpulan data melalui kuisioner. Model Delon and Mclean digunakan sebagai dasar untuk membuat pertanyaan dalam kuisioner dan melakukan pengolahan data kuesioner tersebut. Teknik pengumpulan data menggunakan metoda sampling simple random, dengan populasi mahasiswa STMIK Amik Riau yang aktif pada tahun ajaran 2019-2020 sejumlah 121 orang mahasiswa. Sebelum dilakukan pengolahan, terlebih dahulu data-data tersebut dilakukan uji validitas dan reability. Setelah dilakukan uji validitas dan reability, selanjutnya melakukan uji hipotesis untuk mengetahui hubungan antar variable yang ada pada model Delon and Mc Lean success. Hasil penelitian ini menunjukkan bahwa setiap variable menunjukkan pengaruh hubungan positif dapat diterima seluruhnya dengan nilai 0.000. Artinya semua variable yang digunakan pada penelitian ini memiliki keterkaitan antara satu dengan lainnya. Walaupun demikian perlu diterapkan beberapa strategi yang harus dilakukan dalam upaya pengisian E-EDOM yang sangat objektif oleh mahasiswa.
Kata kunci: e-edom, delon and mclean model, mahasiswa, objektifitas.
Abstract:Tourism is one of the most important factors for the revenue of an area. To attract the interest of tourists takes some supporting factors, one of which is the hotel or the guesthouse. In the process of prediction of tourists…
rists visit, required data the number of tourists staying in order to predict for the next time. The prediction method used in this system is a method of Backpropagation Neural Network based on time series data forecasting component variansi random or random process beginning with autocorrelation for determination of input variables. Method of Backpropagation itself is known quite well used in the forecasting of time series data. The result of the method of Backpropagation is a number of predictions that tourists visit can be a reference for related officials in taking decisions for the period ahead.
Abstract:Cayenne pepper (Capsicum frutescens L.) is a horticultural commodity with high economic value and continuously increasing market demand. The success of cayenne pepper production is largely determined by vegetative growth…
in the early stages, which is influenced by the availability of nutrients and plant growth regulators. The use of manure as a source of organic matter and natural Plant Growth Regulators (PGRs) is expected to enhance vegetative growth. This study aimed to determine the effect of manure application and natural PGRs on the vegetative growth of cayenne pepper and to identify the treatment that produces the best growth.The research was conducted from December to January 2026 at the land of the Kopbun Suka Tani Sejahtera Business Research Center, Kota Juang District, Bireuen Regency. The study used a factorial Randomized Block Design (RBD) with a 4 × 3 pattern and three replications. The first factor was the dosage of manure consisting of 0 kg (control), 1 kg, 1.5 kg, and 2 kg per plant. The second factor was the type of natural PGR, consisting of shallot extract, rice water, and coconut water. The observed parameters included plant height, number of leaves, leaf width, and number of branches at 15, 30, and 45 days after planting (DAP). Data were analyzed using the F-test and continued with the Honestly Significant Difference (HSD) test at a 5% significance level if the results were significant.The results showed that manure application had no significant effect on all vegetative growth parameters up to 45 DAP. The application of natural PGRs also showed no significant effect at 15, 30, and 45 DAP, as well as on other parameters. There was no significant interaction between manure and natural PGRs on all observed parameters.
Abstract:This study aims to determine the effect of manure dosage and types of plant growth regulators (PGRs), as well as their interaction, on the generative phase of bird’s eye chili plants (Capsicum frutescens L.) in order to…
o improve productivity, which remains fluctuating due to suboptimal cultivation practices. The research was conducted from February to March 2026 at the experimental field of the Pusat Riset Bisnis Kopbun Suka Tani Sejahtera, Kota Juang District, Bireuen Regency, using a factorial Randomized Block Design (RBD) of 4 × 3 with three replications. The first factor was the dosage of manure (control, 1 kg/plant, 1.5 kg/plant, and 2 kg/plant), while the second factor was the type of natural PGR (onion extract, rice water, and coconut water). Data were collected through direct observations on several parameters, including number of fruits, fruit weight, fresh biomass weight, root fresh weight, and root length, and were then analyzed to determine the effects of treatments and their interactions.
The results showed that certain doses of manure had a significant effect on increasing yield and root growth, while natural PGRs were able to enhance flowering, reduce flower drop, and accelerate fruit formation. The interaction between both treatments indicated that the optimal combination produced higher results compared to single treatments. The novelty of this study lies in the use of a combination of manure and natural PGRs based on local materials as a strategy to improve the generative phase. The implications of this research are expected to serve as a scientific reference for the development of sustainable cultivation techniques, as well as learning material and further research in the fields of agronomy and horticulture.
Abstract:This study aimed to analyze the response of several cayenne pepper varieties (Capsicum frutescens L.) on the growth and yield of cayenne pepper cultivated in the cocoa (Theobroma cacao L.) alley cropping system. The research…
arch was conducted from April to July 2025 at Mon Jambe Village, Jeumpa District, and the Kopbun Suka Tani Sejahtera Business Research Center, Kota Juang District, Bireuen Regency. The experiment employed a Randomized Block Design (RBD) with four varieties as treatments and three replications, resulting in 12 experimental units. The tested varieties were Rajo, Genie, Bara, and Tetra Hijau. Data were collected through observations of plant height, number of leaves, number of fruits, fruit weight, and fresh biomass weight, and were analyzed using analysis of variance (ANOVA) to determine differences among treatments. The results showed that the varietal factor had a highly significant effect on leaf number and a significant effect on fruit number and fruit weight. The Bara variety exhibited the most adaptive and productive performance under the cocoa alley cropping system, as indicated by superior vegetative growth and yield compared to other varieties. The novelty of this study lies in emphasizing that varietal selection is a key factor in optimizing the utilization of cocoa alley spaces for cayenne pepper cultivation. These findings are expected to provide a scientific basis for developing cayenne pepper cultivation technologies in cocoa alley systems and to serve as a reference for further research on adaptive variety development under such environmental conditions.
Abstract:Penelitian ini bertujuan untuk mengetahui pengaruh kualitas pelayanan dan harga terhadap kepuasan pelanggan pada CV. Tirta Lancar, sebuah perusahaan jasa penyedotan septictank yang beroperasi di wilayah DKI Jakarta. Penelitian…
litian ini menggunakan pendekatan kuantitatif dengan metode survei melalui penyebaran kuesioner kepada 72 responden pelanggan aktif. Teknik pengambilan sampel menggunakan metode sampel random sampling dan analisis data dilakukan menggunakan regresi linear berganda dengan bantuan SPSS versi 26. Hasil penelitian ini menunjukkan bahwa variabel kualitas pelayanan berpengaruh negatif signifikan terhadap kepuasan pelanggan, sedangkan harga berpengaruh positif signifikan terhadap kepuasan pelanggan. Secara simultan, kualitas pelayanan dan harga mampu menjelaskan kepuasan pelanggan. Temuan ini menunjukkan pentingnya perusahaan untuk terus meningkatkan persepsi harga yang sebanding dengan kualitas pelayanan serta melakukan evaluasi terhadap dimensi – dimensi pelayanan yang dirasa masih kurang.