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All Journal Creative Information Technology Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Compiler JURTEKSI CSRID (Computer Science Research and Its Development Journal) EDUMATIC: Jurnal Pendidikan Informatika Budimas : Jurnal Pengabdian Masyarakat Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Mandiri IT Abdiformatika: Jurnal Pengabdian Masyarakat Informatika IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Pelayanan dan Pengabdian Masyarakat Indonesia Eduvest - Journal of Universal Studies Jurnal Pengabdian Bisnis dan Akuntansi Soedirman Jurnal INFOTEL Repeater: Publikasi Teknik Informatika dan Jaringan
Paradise, Paradise
Institut Teknologi Telkom Purwokerto
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 1 
TRAVELLING SALESMAN PROBLEM (TSP) OPTIMIZATION SEED DIS-TRIBUTION USING GENETIC ALGORITHM Vera Wati; Yuliana Yuliana; Paradise Paradise; Kusrini Kusrini
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 8, No 3 (2022): Agustus 2022
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v8i3.1738

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
DETECTION OF CHILDREN'S NUTRITIONAL STATUS USING MACHINE LEARNING WITH LOGISTIC REGRESSION ALGORITHM Yuliana Yuliana; Paradise Paradise; Mudawil Qulub
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 2 (2024): Maret 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2973

Abstract

Abstract: Children's nutritional issues are an important concern for parents to pay attention to growth and development, especially health and well-being. According to the results of the Ministry of Health's Indonesian Nutrition Status Survey (SSGI), there are 4 nutritional problems for children in Indonesia, namely stunting, wasting, underweight and everweight. In this research, how to predict signs of symptoms of a decline in a child's nutritional status using a machine learning algorithm, a prediction model was designed using logistic regression in Python IDE to predict whether a child is indicated by a decline in nutrition or not. Dataset from Bengkayang Community Health Center data consisting of 657 pediatric patient data. The dataset is divided into 7 features (independent variables) and 1 predictor (dependent variable). Test results show perfect performance with precision, recall, F1-score, accuracy values of 100%. Then the visualization results on the ROC (Receiver Operating Characteristic) curve to depict the TP (True Positive) value on the Y axis against the FP (false Positive) value on the become overfit. It is recommended that in preparing the training dataset, measure the training data and reduce the features, after carrying out feature selection to increase the accuracy of the model.            Keywords: child nutritional status; growth and development logistic regression; machine learning Abstract: Masalah Gizi anak menjadi perhatian penting bagi orangtua untuk memperhatikan tumbuh kembang, terutama kesehatan dan kejahteraan. Menurut hasil survei status Gizi Indonesia (SSGI) Kemenkes memperlihatkan 4 permasalahan gizi anak di Indonesia yaitu stunting, wasting, underweight, dan everweight. Dalam penelitian ini, bagaimana memprediksi tanda gejala penurunan status gizi anak menggunakan  algoritma  machine  learning dirancang model prediksi menggunakan logistic regression pada Python IDE dengan  memprediksi anak  terindikasi  penurunan gizi  atau tidak. Dataset dari data Puskesmas Bengkayang  yang terdiri 657 data pasien anak. Dataset dibagi menjadi 7 feature (variabel independen) dan 1 predictor (variabel dependen). Hasil Pengujian memperlihatkan kinerja yang sempurna dengan nilai presisi, recall,  F1-score, akurasi, sebesar 100%. Kemudian hasil Visualisasi pada kurva ROC (Receiver Operating Characteristic) untuk menggambarkan nilai TP (True Positif) di sumbu Y terhadap nilai FP (false Positif) di sumbu X juga menunjukkan nilai yang sangat tinggi dan sudah mendekati angka 1 ini pertanda bahwa model ini menjadi overfit. Sebaiknya dalam persiapan training dataset diukur dengan data training dan mengurangi feature, setelah melakukan feature Selection untuk meningkatkan akurasi model. Keywords: logistic regression; machine learning; status gizi anak; tumbuh kembang
DETECTION OF CHILDREN'S NUTRITIONAL STATUS USING MACHINE LEARNING WITH LOGISTIC REGRESSION ALGORITHM Yuliana, Yuliana; Paradise, Paradise; Qulub, Mudawil
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2973

Abstract

Abstract: Children's nutritional issues are an important concern for parents to pay attention to growth and development, especially health and well-being. According to the results of the Ministry of Health's Indonesian Nutrition Status Survey (SSGI), there are 4 nutritional problems for children in Indonesia, namely stunting, wasting, underweight and everweight. In this research, how to predict signs of symptoms of a decline in a child's nutritional status using a machine learning algorithm, a prediction model was designed using logistic regression in Python IDE to predict whether a child is indicated by a decline in nutrition or not. Dataset from Bengkayang Community Health Center data consisting of 657 pediatric patient data. The dataset is divided into 7 features (independent variables) and 1 predictor (dependent variable). Test results show perfect performance with precision, recall, F1-score, accuracy values of 100%. Then the visualization results on the ROC (Receiver Operating Characteristic) curve to depict the TP (True Positive) value on the Y axis against the FP (false Positive) value on the become overfit. It is recommended that in preparing the training dataset, measure the training data and reduce the features, after carrying out feature selection to increase the accuracy of the model.            Keywords: child nutritional status; growth and development logistic regression; machine learning Abstract: Masalah Gizi anak menjadi perhatian penting bagi orangtua untuk memperhatikan tumbuh kembang, terutama kesehatan dan kejahteraan. Menurut hasil survei status Gizi Indonesia (SSGI) Kemenkes memperlihatkan 4 permasalahan gizi anak di Indonesia yaitu stunting, wasting, underweight, dan everweight. Dalam penelitian ini, bagaimana memprediksi tanda gejala penurunan status gizi anak menggunakan  algoritma  machine  learning dirancang model prediksi menggunakan logistic regression pada Python IDE dengan  memprediksi anak  terindikasi  penurunan gizi  atau tidak. Dataset dari data Puskesmas Bengkayang  yang terdiri 657 data pasien anak. Dataset dibagi menjadi 7 feature (variabel independen) dan 1 predictor (variabel dependen). Hasil Pengujian memperlihatkan kinerja yang sempurna dengan nilai presisi, recall,  F1-score, akurasi, sebesar 100%. Kemudian hasil Visualisasi pada kurva ROC (Receiver Operating Characteristic) untuk menggambarkan nilai TP (True Positif) di sumbu Y terhadap nilai FP (false Positif) di sumbu X juga menunjukkan nilai yang sangat tinggi dan sudah mendekati angka 1 ini pertanda bahwa model ini menjadi overfit. Sebaiknya dalam persiapan training dataset diukur dengan data training dan mengurangi feature, setelah melakukan feature Selection untuk meningkatkan akurasi model. Keywords: logistic regression; machine learning; status gizi anak; tumbuh kembang
Co-Authors Ahmad, Muhammad Aliif Aiza Yudha Pratama Alika, Shintia Dwi Amalia Beladinna Arifa Arif Budiman Asro Nasiri Aufa Salsabila Nahrowi Aulia Burhanuddin Auliya Burhanuddin Buananta, Septian Eka Ady Cahya, Virdies Nur C Chusnul Hidayat Chusnul Maulidina Hidayat Eva Rahma Indriyani Gladi Pawestri Utami I Anna Tul Munikhah Khusnul Fauziah Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Laila Isyriyah Mahmood, Jamilah Maliana Puspa Arum Merlinda Wibowo Merlinda Wibowo Merlinda Wibowo Merlinda Wibowo Mudawil Qulub Muhammad Afrizal Amrustian Muhammad Eka Purbaya Muhammad Yusril Aldean Munthe, Adrian Wismar Novanda Alim Setya Nugraha Pawit Widiyantoro Qulub, Mudawil Rakhmad Maulidi Rifki Adhitama, Rifki Riqqah Fadiyah Alya Saputra, Sandra Sarah Astiti S.Kom., M.MT Setyanto, Refius Shaquille Akbar Demsi Silvia Van Marsally Teguh Rijanandi Veithzal Rivai Zainal Vera Wati Wahyu Adi Prabowo Wahyu Adi Prabowo wahyu prabowo Wibowo, Fahrudin Mukti Yogo Dwi Prasetyo Yuliana Yuliana Yuliana Yuliana Yuliana Yuliana, Yuliana