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All Journal JUITA : Jurnal Informatika JIEET (Journal of Information Engineering and Educational Technology) ILKOM Jurnal Ilmiah Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Informatika Global Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal Generic RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Pengembangan Sistem Informasi dan Informatika Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal INFOTEL Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Networks and IoT Global SISFOTENIKA
Nazori Suhandi
Fakultas Ilmu Komputer, Universitas Indo Global Mandiri
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Toddlers’ Nutritional Status Prediction Using the Multinomial Logistics Regression Method Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino; Dewi Sartika
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3372

Abstract

Malnutrition is one of the foremost health problems experienced by children under five in many countries, especially in low and middle-income countries. Meanwhile, the target of Sustainable Development Goals (SDGs) 2.2 is that all forms of malnutrition must end by 2025. Therefore, this study aims to predict the toddlers’ nutritional status (malnutrition, undernutrition, overnutrition, and normal nutrition) based on age, body mass index (BMI), weight, and length using the Multinomial Logistic Regression (MLR) classification method. The dataset consists of two hundred toddlers obtained from the Kaggle site. Following pre-processing, the dataset is divided, with 80 percent of the data for training and the remaining 20 percent for testing. The model was trained using 10-fold cross-validation (CV). In Addition, the MLR model performance was evaluated using the confusion matrix (CM), the area under the curve (AUC), and the Kappa coefficient (KC). The evaluation results using CM show that the accuracy, sensitivity, and specificity values are 0.9412, 0.9375, and 0.9790, respectively. AUC and KC also show excellent results. It indicates that the MLR method is an esteemed and recommended method for predicting the nutritional status of toddlers. Therefore, this research can contribute to providing early information so that the Government can immediately determine the necessary treatment.
Pelatihan Penggunaan Aplikasi Reservasi Kamar Hotel Untuk Meningkatkan Layanan Konsumen Nazori Suhandi; Rendra Gustriansyah
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 7, No 2 (2024): April 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v7i2.2938

Abstract

The travel and tourism sectors are closely related to the hospitality sector. The success of a hotel in this digital era is strongly supported by customer service that utilizes information technology-based applications. However, using these applications effectively requires proper training and understanding. Therefore, this service activity aims to train Swarna Dwipa hotel staff in operating a web-based hotel room reservation application. It is one way for hotel staff to provide optimal service and assist in driving business success in the hospitality sector. Many studies show that operating hotel room reservation applications can increase customer satisfaction and efficiency in the hotel business. However, proper and systematic training is necessary to maintain competitiveness. The stages of this activity include observation, interviews, sharing knowledge, training in application use, and evaluation. The simulation results show that participants can use the reservation application in a structured and systematic manner with a significant level of user acceptance of applications. Keywords: application; hotel; training; reservation
Tree-based models and hyperparameter optimization for assessing employee performance Gustriansyah, Rendra; Puspasari, Shinta; Sanmorino, Ahmad; Suhandi, Nazori; Sartika, Dewi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp569-577

Abstract

The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Toddlers’ Nutritional Status Prediction Using the Multinomial Logistics Regression Method Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad; Sartika, Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3372

Abstract

Malnutrition is one of the foremost health problems experienced by children under five in many countries, especially in low and middle-income countries. Meanwhile, the target of Sustainable Development Goals (SDGs) 2.2 is that all forms of malnutrition must end by 2025. Therefore, this study aims to predict the toddlers’ nutritional status (malnutrition, undernutrition, overnutrition, and normal nutrition) based on age, body mass index (BMI), weight, and length using the Multinomial Logistic Regression (MLR) classification method. The dataset consists of two hundred toddlers obtained from the Kaggle site. Following pre-processing, the dataset is divided, with 80 percent of the data for training and the remaining 20 percent for testing. The model was trained using 10-fold cross-validation (CV). In Addition, the MLR model performance was evaluated using the confusion matrix (CM), the area under the curve (AUC), and the Kappa coefficient (KC). The evaluation results using CM show that the accuracy, sensitivity, and specificity values are 0.9412, 0.9375, and 0.9790, respectively. AUC and KC also show excellent results. It indicates that the MLR method is an esteemed and recommended method for predicting the nutritional status of toddlers. Therefore, this research can contribute to providing early information so that the Government can immediately determine the necessary treatment.
Pendampingan Implementasi E-Arsip Untuk Proyek Infrastruktur Tol Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad; Wiyanto, Ari
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 8, No 2 (2025): April 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i2.3605

Abstract

The conventional management of archives using physical files at the Jambi-Betung II Toll Road Land Procurement Commitment Making Officer (PPK-PPTJT) agency results in slow document retrieval, a higher risk of data loss, and limited accessibility to important information. This community service initiative aims to enhance the technological skills of human resources at PPK-PPTJT Jambi-Betung II, particularly in electronic archive management. The methods employed involve socialization and technical training on using e-archive applications for four PPK-PPTJT employees. Evaluation was conducted through questionnaires and interviews to assess the participants' improvement in understanding and skills. The results demonstrated a significant increase in participants' capabilities: 25% reported a better understanding of the benefits of e-archives, 75% enhanced their operational application skills, and 25% felt more confident in managing electronic archives. The implementation of e-archives has successfully reduced reliance on physical documents and expedited the toll road land procurement administration process, ultimately increasing the operational efficiency of PPK-PPTJT Jambi-Betung II.Keywords: e-archive; mentoring; land procurement; archive management  Abstrak: Pengelolaan arsip secara konvensional dengan menggunakan berkas fisik di instansi Pejabat Pembuat Komitmen Pelaksana Pengadaan Tanah Jalan Tol (PPK-PPTJT) Jambi-Betung II menyebabkan lambatnya pencarian dokumen, rentan kehilangan data, dan terbatasnya aksesibilitas terhadap informasi penting. Tujuan pengabdian ini adalah untuk meningkatkan kapasitas sumber daya manusia di PPK-PPTJT Jambi-Betung II dalam hal digitalisasi dan pengelolaan arsip elektronik. Metode yang digunakan meliputi sosialisasi, pelatihan teknis penggunaan aplikasi e-arsip bagi empat pegawai PPK-PPTJT. Evaluasi dilakukan dengan angket dan wawancara untuk mengukur peningkatan pemahaman dan keterampilan empat peserta. Hasil evaluasi menunjukkan peningkatan signifikan: 19% peserta lebih memahami manfaat e-arsip, 31% peningkatan kemampuan operasional aplikasi, dan 25% peningkatan kepercayaan diri dalam pengelolaan arsip elektronik. Penerapan e-arsip berhasil mengurangi ketergantungan pada arsip fisik, mempercepat proses administrasi pengadaan tanah jalan tol, efisiensi ruang penyimpan, dan kemudahan monitoring dan evaluasi proses operasional PPK-PPTJT Jambi-Betung II.Kata kunci: e-arsip; pendampingan; pengadaan tanah; pengelolaan arsip
Klasifikasi Data Kelulusan Siswa SMK Muhammadiyah 1 Palembang Menggunakan Metode Naive Bayes Ramadhani, Kiki; Suhandi, Nazori; Irfani, Muhammad Haviz
Journal Of Intelligent Networks and IoT Global Vol 3 No 1 (2025)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v3i1.5903

Abstract

Perkembangan saat ini semakin pesat seiring dengan berkembangnya teknologi informasi, untuk membantu karyawandalam menyelesaikan tugasnya dan menjamin efisiensi waktu. Didalam dunia pendidikan, penerapan data miningmemberikan peluang besar untuk membantu sekolah dan perguruan tinggi, baik negeri maupun swasta dalam memperolehwawasan yang berguna. Salah satu klasifikasi yang cocok digunakan dalam menyeleksi informasi alumni lebih lanjutadalah metode Naive Bayes karena memiliki tingkat kecepatan dan akurasi yang lebih tinggi sehingga mampu menangkapdata lebih banyak dibandingkan metode lainnya. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes dalamklasifikasi data lulusan siswa SMK Muhammadiyah 1 Palembang. Dataset diperoleh dari Wakil Kepala Sekolah SMKMuhammadiyah 1 Palembang 562 data. Hasil penelitian menunjukkan bahwa data training sebanyak 393 data denganalgoritman Naïve Bayes berhasil memprediksi besarnya kelulusan mahasiswa dengan presentase keakuratan sebesar85,80%. Data mining dan naïve bayes mampu menampilkan informasi prediksi kelulusan siswa dengan menggunakandata siswa yang telah lulus sebagai data training dan data testing. Sebanyak 169 data testing yang dihasilkan penelitianini bahwa siswa yang lulus sebanyak 110 siswa atau sekitar 65% dari jumlah data testing sebesar 85,80%. Berdasarkanhasil ini, metode naïve bayes direkomendasikan untuk klasifikasi data dalam memprediksi kelulusan siswa.
Marketing Strategy Using Frequent Pattern Growth Suhandi, Nazori; Gustriansyah, Rendra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1039

Abstract

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.
Metode Pembelajaran Mesin untuk Memprediksi Status Gizi Balita Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.988

Abstract

Malnutrition is one of the leading health problems experienced by toddlers in various countries. Based on the 2022 Indonesian Nutritional Status Survey results, malnutrition in children under five in Indonesia is higher than the average malnutrition in Africa and globally. Therefore, a way is needed to predict the nutritional status of children under five early and quickly so that the Government (through District Health Office) can immediately provide the necessary treatment. This study aims to predict or classify the toddlers' nutritional status based on age, body mass index (BMI), weight, and body length using various machine learning (ML) methods, namely naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbor, random forest, and support vector machine. The predictive performance of each ML method was evaluated based on accuracy, sensitivity, specificity, the area under curve, and Cohen's Kappa coefficient. The test results show that the RF method is the most recommended for predicting toddlers' nutritional status. The study's contribution is to obtain information about toddlers' nutritional status easier.
Prediksi Angka Kemiskinan Desa Kemang Bejalu Menggunakan Metode Naïve Bayes Damayanti, Arda; Puspasari, Shinta; Suhandi, Nazori
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 5 No. 3 (2024): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v5i3.1737

Abstract

Poverty is one of the problems faced by all countries, especially developing countries like Indonesia. A study was conducted using the Naive Bayes method to determine the extent of poverty in Kemang Bejalu Village. The Naive Bayes method is used to classify data and calculate the probability of poverty based on certain factors. This research aims to determine whether the accuracy of the results of the Naive Bayes method can be used to predict poverty rates. Calculation of the confusion matrix obtained an accuracy of 86% from 258 data for 3 variables, while in testing the new test data, an accuracy of 90% was obtained using the same variables (i.e., dependents, work, and income). Based on population data for 2022 where, as many as 33% of poor families are able, while as many as 67% of capable families are used to produce a poverty rate of 76% of capable families and 24% of incapable families with a test size of 0.4.
Prediksi Kualitas Susu Menggunakan Metode K-Nearest Neighbors Suhandi, Nazori; Gustriansyah, Rendra; Destria, Abel; Amalia, Marshanda; Kris, Via
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i2.430

Abstract

Milk is a nutrient-rich source abundant in calcium and lactose, playing a crucial role in addressing nutritional deficiencies. Milk quality is determined by pH levels and pasteurization processes. This research aims to predict milk quality using the K-Nearest Neighbors (K-NN) Method. The analysis is conducted through a series of steps, including data preprocessing involving categorical data encoding, handling missing values, and data cleansing. Subsequently, the optimal K value is selected using the elbow method, with a value of K=3. The data is then divided into training and testing sets to avoid overfitting and validate model performance, and the testing results of using K-NN to predict milk quality are evaluated using three different data splitting schemes: 80-20, 70-30, and 60-40. By utilizing Confusion Matrix to calculate precision, recall, and accuracy, we can assess the proportion of correctly classified positive cases, accurately identified. The best accuracy result is obtained from scheme one at 0,94, with a recall of 0.8, and precision reaching 1. This research provides a significant contribution to understanding, predicting, and monitoring milk quality, encompassing a profound understanding of factors influencing milk quality and the development of advanced predictive models. Overall, this study strengthens the scientific foundation for the dairy industry comprehensively.
Co-Authors Ahmad Sanmorino Amalia, Marshanda Antony, Fery Apri Aidila Utami Arda Damayanti Ariesta Diana Putri Damayanti, Arda Destria, Abel DEWI SARTIKA Efri Ayu Kartika Putri Fery Antony Hastha Sunardi Imelda Saluza, Imelda Indah Charista Irma Yuliawati Jhons Fransdesker John Roni Coyanda Juhaini Alie Kanaka, Nayaka Al Syahreal Kris, Via Masda Rahadian Muhammad Haviz Irfani Rachmansyah Rachmansyah Ramadhani, Kiki Ratna Meilinda Rendra Gustriansyah Sari Agnisa Sartika, Dewi Shinta Puspasari Shinta Puspasari Sri Primaini Sunardi, Hastha Sunardi, Hastha Suzan Agustri Wiyanto, Ari