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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Information Technology and Computer Science INTEGER: Journal of Information Technology JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Artificial Intelligence and Robotics (IJAIR) Journal Of Artificial Intelligence And Software Engineering
Mohammad Zoqi Sarwani, Mohammad Zoqi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier Sarwani, Mohammad Zoqi; Salafudin, Muhammad Shubkhan; Sani, Dian Ahkam
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2169.831 KB) | DOI: 10.25139/ijair.v2i1.2636

Abstract

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.
An Implementation of MMS Steganography With The LSB Method Sani, Dian Ahkam; Sarwani, Mohammad Zoqi; Setiawan, Muhamad Agus
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1378.483 KB) | DOI: 10.25139/ijair.v2i1.2653

Abstract

Around the world, the internet (interconnection network) has developed into one of the most popular data communication media. With a variety of illegal information retrieval techniques that are developing, many people are trying to access information that is not their right. Various techniques to protect confidential information from unauthorized persons have been carried out to secure important data. Steganography is a science and art for writing hidden messages so that no other party knows the existence of the message. The three results of tests conducted by the LSB method can be used to hide messages into images. The first test was successful by writing a message that less than 31 characters stored in the picture, the second succeeded in writing a message equal to 31 characters stored in the picture, the third failed to write a message of more than 31 characters stored in the picture.
Rekomendasi Pengobatan Pada Penyakit Kucing Menggunakan Metode Decision Tree (Studi Kasus : Klinik Drh. Panti Absari) Hidayat, Faris; Sarwani, Mohammad Zoqi; Hariyanto, Rudi
INTEGER: Journal of Information Technology Vol 9, No 2: September 2024
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v9i2.6319

Abstract

Kucing adalah salah satu hewan peliharaan  paling populer di dunia. Dalam merawat kucing, kesehatan menjadi faktor yang harus diperhatikan saat merawat kucing. Penyakit kucing merupakan isu penting dalam dunia kesehatan hewan. Seiring kemajuan teknologi, pengobatan yang direkomendasikan mengalami berubah secara signifikan. Sistem kecerdasan buatan dan analisis data yang canggih memungkinkan para profesional medis membuat rekomendasi pengobatan yang lebih personal dan efektif. Dalam dunia medis, deteksi dan diagnosis penyakit sangatlah penting. Metode prediksi ini membantu pemilik kucing mengetahui  pengobatan terbaik dan benar. Salah satu metode yang dapat digunakan adalah metode Decision Tree. Metode Decision Tree digunakan untuk mempelajari klasifikasi dan prediksi dari data serta menggambarkan hubungan antara variabel x dan variabel y dalam bentuk pohon. Hasil dari pengujian dengan menggunakan 100 data yang dibagi menjadi 80 data training dan 20 data testing menunjukkan tingkat akurasi yang cukup tinggi yaitu 90%, Precision 83%, dan recall 90%.
Implementasi Association Rule Pada Transaksi Penjualan Menggunakan Metode Neural Network Backpropagation Putri, Nabila; Sarwani, Mohammad Zoqi; Hariyanto, Rudi
INTEGER: Journal of Information Technology Vol 9, No 2: September 2024
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v9i2.6398

Abstract

Pertumbuhan perdagangan dan penjualan telah menghasilkan volume data transaksi yang besar dan kompleks. Analisis data transaksi dapat memberikan wawasan berharga tentang pola pembelian pelanggan dan asosiasi antara item yang dibeli. Dalam penelitian ini, kami mengusulkan pendekatan baru untuk mengekstraksi aturan asosiasi dari data transaksi penjualan menggunakan metode Neural Network Backpropagation. Pendekatan ini mengintegrasikan kekuatan jaringan saraf untuk memodelkan pola kompleks dalam data transaksi untuk menemukan hubungan antara item yang dibeli. Pertama, data transaksi diubah menjadi representasi biner yang sesuai untuk pemrosesan oleh jaringan saraf. Selanjutnya, jaringan saraf feedforward dengan lapisan tersembunyi diinisialisasi dan dilatih menggunakan algoritma backpropagation. Kami menguji pendekatan kami menggunakan dataset transaksi penjualan dari data di PT. Program Induk Utama. Hasil percobaan menunjukkan bahwa pendekatan yang diusulkan mampu menemukan aturan asosiasi yang signifikan dan bermakna dalam dataset penjualan, dengan tingkat keakuratan 92,30%, epoch 1312, dan MSE 0,0009979927. Selain itu, pendekatan ini menawarkan kelebihan dalam menangani pola-pola yang kompleks dan tidak terstruktur dalam data transaksi.
Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm Sarwani, Mohammad Zoqi; Mahmudy, Wayan Firdaus; Naba, Agus
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.249 KB) | DOI: 10.25126/jitecs.20161218

Abstract

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorithm
Prediksi Hasil Tangkapan Ikan di Kota Pasuruan Dengan Metode Support Vector Machine (SVM) Chanafi, Imam; Sarwani, Mohammad Zoqi; Udin, Muhammad
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7163

Abstract

 Kota Pasuruan yang terletak di Provinsi Jawa Timur merupakan kota yang kaya akan potensi dan beragam sumber daya serta mempunyai peran penting dalam sektor industri dan perdagangan. Untuk mengelola dan mengembangkan potensi ini, Dinas Perairan Kota Pasuruan memiliki tugas menjaga keseimbangan ekosistem perikanan sekaligus memberdayakan para nelayan dan pembudidaya ikan. Tujuan dari penelitian ini adalah untuk mengetahui prediksi hasil tangkapan ikan di Kota Pasuruan menggunakan Metode Support Vector Machine (SVM). Data yang diperoleh dari Dinas Perikanan Kota Pasuruan dalam waktu 5 tahun mulai tahun 2019 sampai 2023. Dari hasil penelitian ini, maka dapat disimpulkan hasil nilai MAPE Train Linear 18.07%, MAPE Test Linear 22.92%. Sedangkan untuk MAPE Train Polynominal 16.95%, MAPE Test Polynominal 18.46%.
Prediction of Stunting Nutritional Status in Toddlers Using Naïve Bayes Classifier Algorithm Hariyanto, Rudi; Sarwani, Mohammad Zoqi; Aprilia, Yunita Nur
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.5930

Abstract

Stunting is a chronic nutritional problem in toddlers that affects children's physical growth and cognitive development. Early identification and prediction of toddlers' nutritional status are crucial for timely intervention. This study aims to predict the nutritional status of stunting in toddlers using the Naïve Bayes Classifier algorithm. The data used in this study is derived from community health surveys with variables such as age, weight, height, and parental nutritional status. The research process began with data collection and pre-processing to ensure high-quality data. Subsequently, the data was trained using the Naïve Bayes Classifier algorithm, known for its simplicity and efficiency in data classification. Prediction results were then evaluated using metrics of accuracy, precision, recall, and F1-score to measure the model's performance. The study results indicate that the Naïve Bayes Classifier algorithm has high accuracy in predicting stunting status in toddlers, with an accuracy rate of 85%. Precision and recall also showed satisfactory results, at 82% and 87%, respectively. This model can be used as a tool for health workers to identify toddlers at risk of stunting, enabling earlier preventive measures. In conclusion, the use of the Naïve Bayes Classifier algorithm is proven effective in predicting the nutritional status of stunting in toddlers. The implementation of this model is expected to support child health programs and accelerate the reduction of stunting prevalence in the community.
Optimization of the Naïve Bayes Classifier Algorithm Using Cost-Sensitive Learning to Detect Lung Diseases with an Imbalanced Dataset sarwani, mohammad zoqi; Khoiron, Mohamad; Udin, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6474

Abstract

Lung diseases are one of the global public health issues that continue to be a primary concern in the medical field. According to data from the World Health Organization (WHO), 91% of the world’s population lives in areas with poor air quality. Continuous exposure to dust, cigarette smoke, air pollutants, and toxic chemicals can increase the risk of developing lung diseases. In efforts to reduce the health impacts on the lungs and assist doctors in classifying lung diseases, a method is needed to predict lung diseases. Naïve Bayes is a classification technique that uses probability and statistics. This research uses a dataset of 30,000, which is divided into training data and testing data, with 80% allocated for training and 20% for testing. The results of this study show that optimization performed on the Naïve Bayes algorithm using cost-sensitive learning achieved an accuracy of 79.6%, which represents a 12% improvement in accuracy compared to the previous result without optimization.
IMPLEMENTATION OF THE BACKPROPAGATION METHOD FOR RECOMMENDING ANNUAL AWARD RECIPIENTS AMONG OUTSTANDING STUDENTS Romadhona, Salsabil Wahyu; Sarwani, Mohammad Zoqi; Widodo, Anang Aris
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7978

Abstract

This research aims to develop a recommendation system for annual awards for outstanding students using the Backpropagation method in an Artificial Neural Network (ANN). Student assessment is based on four main variables: academic grades, attitude scores, extracurricular activity scores, and attendance records. The data were obtained from an elementary school in Pasuruan City through a survey method, then processed using preprocessing and normalization techniques before being trained using the Backpropagation algorithm. The model was developed using a Sequential architecture with two hidden layers, and its performance was evaluated using a confusion matrix and a classifi-cation report. The testing results showed that the model was able to classify outstanding students with a highest accuracy rate of 97%, demonstrating strong performance in terms of precision, recall, and F1 score. These results indicate that the Backpropagation method is effec-tive in enhancing the objectivity and efficiency of the outstanding stu-dent selection process based on historical data.
Analysis Of Bread Demand Forecasting Using Recurrent Neural Network (RNN) Method Based On Operational Delivery Data Saputro, Harinudin; Sarwani, Mohammad Zoqi; Hariyanto, Rudi
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13507

Abstract

Accurate demand forecasting plays a vital role in optimizing inventory and distribution planning, especially for perishable goods such as bread. This study develops a time series forecasting model using a Recurrent Neural Network (RNN) with a Sequential architecture to predict daily bread demand. Unlike previous research, this model is trained on two years of real operational delivery data (2023–2024), enabling it to capture actual consumption patterns more effectively. The model leverages a 7-day sequence window to predict the next day’s demand, reflecting weekly seasonality. Data preprocessing includes normalization and cleaning, followed by training with the Stochastic Gradient Descent (SGD) optimizer. The model achieved a Mean Absolute Percentage Error (MAPE) of 4.88% and an accuracy of 86.90%, demonstrating high predictive performance and robustness in handling fluctuating, real-world data. The implementation of this model provides a practical solution for improving production planning, reducing waste, and enhancing supply chain responsiveness. The findings confirm that RNN-based models are effective tools for demand forecasting in dynamic business environments.   Keywords - Forecasting, Recurrent Neural Network (RNN), Demand Prediction, Operational Delivery Data, Bread Industry.
Co-Authors Agus Naba Anang Aris Widodo Aprilia, Yunita Nur Chanafi, Imam Hidayat, Faris Mohamad Khoiron Nadhiroh, Irni Irana Ainin Putri, Nabila Romadhona, Salsabil Wahyu Rudi Hariyanto Salafudin, Muhammad Shubkhan Sani, Dian Ahkam Saputro, Harinudin Setiawan, Muhamad Agus Suharyadi, Heri Udin, Muhammad Wayan Firdaus Mahmudy