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Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 30248167     EISSN : 31098959     DOI : https://doi.org/10.58776/jriti.v3i1
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika dan Teknologi Informasi merupakan jurnal ilmiah yang diterbitkan oleh Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) Banten. Jurnal ilmiah ini memuat hasil riset dosen, peneliti, mahasiswa dan masyarakat umum dibidang informatika dan teknologi informasi serta rumpun dan turunannya. Jurnal ini terbit tiga kali dalam setahun. Terbitan pertama di bulan Agustus 2023. Sedangkan untuk periode terbit adalah Agustus, Desember, dan April. Adapun bidang riset yang menjadi fokus jurnal ini (dengan tanpa bermaksud membatasi) adalah terkait dengan topik : data mining, data science, pembelajaran mesin (machine learning), kecerdasan buatan, sistem pakar, sistem informasi manajemen, sistem pendukung keputusan, cyber security, soft computing, logika samar (fuzzy logic), pengenalan pola, computer vission, pengolahan citra digital, software engineering, manajemen proyek, software testing, dan topik lain terkait informatika dan teknologi informasi yang relevan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 49 Documents
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Analisis Sentimen Terhadap Ulasan Aplikasi Pinjaman Online (PINJOL) di Google Play Store Menggunakan Naive Baiyes Classifier Alta Zahir, Muhamad Rafli; Ramdani, Bagus; Dwi Saputra, Arya
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 2 (2024): Desember 2023 - Maret 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i2.39

Abstract

Penelitian ini bertujuan untuk mendapatkan ulasan pengguna pada aplikasi pinjaman online melalui aplikasi Kredivo di Google Play Store. Analisis sentimen ini menggunakan algoritma Naive Baiyes Classifier yang memiliki nilai keakuratan yang cukup tinggi. Data yang diambil merupakan ulasan Positif maupun Negatif.
Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City ardhiyanto, ozzi; Ajif Yunizar Pratama Yusuf, S.Si, M.Eng; Salam Asyidqi, Muhammad; Dr. Tb. Ai Munandar, S.Kom., MT
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 1 (2024): Agustus - November 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i1.41

Abstract

Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.
Deteksi Status Internal Battery UPS Berdasarkan Hasil Pengukuran Resistansi Dari Battery Tester Menggunakan Algoritma C4.5 Kusumah, Muhammad Assegaf Raja; Setyawati, Ananda; Wardana, Muhammad Bisma Arya; Tb Ai Munandar; Ajif Yunizar Pratama Yusuf
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 1 (2024): Agustus - November 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i1.44

Abstract

The purpose of this research is to propose a classification model using the decision tree method that can detect the internal status of UPS batteries by using resistance measurements from a battery tester. Resistance data of UPS batteries were collected from measurements conducted under various conditions. This study focuses on supervised learning models, where the data is processed to form a decision tree using the C4.5 classification algorithm. The test results show that the classification of UPS battery internal status can be achieved with high accuracy. The data presentation results were obtained from 100 units of battery systems in UPS, with two conditions identified: 38 units of batteries with a Normal condition and a presentation data accuracy rate of 100%, and 62 units of batteries with a Fault condition and a presentation data accuracy rate of 100%. By using this classification model, users can monitor the performance of the internal battery in the UPS system and take necessary actions to maintain stable UPS system operation and usage.
Implementasi Metode Arima Data Warehouse Untuk Prediksi Permintaan Suku Cadang Hendrik Hidayatullah; Fitri Sukaesih; Yanuar Arif Hizbulloh; Tb Ai Munandar
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.48

Abstract

Food production company is a company that focuses on the production of instant noodles, and machine reliability is crucial in the production process. To maintain machine reliability, regular maintenance is necessary, and the availability of spare parts is also a key factor in reliability planning. Therefore, spare part management is crucial in the company as it can affect the spare part control system and vice versa. Poor spare part management planning can result in fluctuations in demand for goods. Uncertainty forces the company to determine the minimum and maximum spare parts inventory to be managed. Lack of standards during spare part deliveries leads to excess spare parts. Excess spare parts cause inventory to accumulate in the workshop. However, if there is a shortage of spare parts, it makes maintenance difficult in the production department. Based on the data used, this research is classified as quantitative research that produces numbers. The aim of this research is to predict the demand for spare parts for maintenance processes using the ARIMA (Autoregressive Integrated Moving Average) method. This research is carried out because the proper and effective use of spare parts is essential in maintaining machine and industrial equipment reliability. The ARIMA method is used to identify patterns in spare part demand data and make accurate predictions for future demand. Spare part demand data for a certain period of time is collected and analyzed using statistical software. The results of the research show that the ARIMA method can be used to predict spare part demand with a high level of accuracy. With this prediction, the company can better plan to meet demand and optimize spare part inventory management. The results of this research can provide benefits to the company in improving their operational efficiency and effectiveness while reducing costs related to spare part inventory shortages.
Klasifikasi Postingan Pengguna Facebook Untuk Deteksi Phising Menggunakan Naive Bayes Nur Fadlilah, Fikki Arsyi; Fahmi, Muhammad
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 2 (2024): Desember 2023 - Maret 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i2.49

Abstract

Phising merupakan penipuan digital yang umum dilakukan oleh penjahat siber dengan tujuan untuk mengambil data informasi pribadi pengguna dengan cara memanipulasi. Facebook adalah platform media social yang sangat popular di dunia sehingga bisa menjadi tempat yang basah bagi penjahat siber phising. Pada penelitian kali ini, kami membangun model Klasifikasi untuk mengidentifikasi dan mencegah upaya phising pada postingan facebook. Dataset yang digunakan dalam penelitian ini diperoleh dari posting pengguna facebook yang dikumpulkan. Pengolahan data dilakukan dengan melakukan preprocessing pada teks posting, termasuk penghapusan tanda baca dan kata kata yang tidak penting. Metode yang digunakan adalah Naïve Bayes untuk mengklasifikasikan posting kedalam kategori phising atau tidak phising. Metode Naïve Bayes digunakan karena kemampuannya dalam mengklasifikasikan data dengan tingkat Akurasi yang baik. Hal ini menunjukan bahwa fitur fitur yang dipilih dalam penelitian ini dapat menjadi indicator yang kuat untuk mendeteksi phising pada postingan pengguna facebook. Hasil penelitian menunjukan Naïve Bayes dapat menjadi solusi yang efektif untuk deteksi phising pada postingan pengguna facebook. Selain itu, hasil dari penelitian ini dapat memberikan wawasan yang berharga tentang ciri ciri umum dari postingan phising pada Facebook. Dengan nilai akurasi sebesar 99,01% diharapkan penelitian ini dapat membantu meningkatkan kesadaran dan keamanan Pengguna Facebook terhadap postingan phising.
Klasifikasi Postingan Pengguna Facebook Untuk Deteksi Phising Menggunakan Naive Bayes Muhammad Fahmi; Nur Fadlillah, Fikki Arsyi
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.53

Abstract

Phishing is a digital fraud that is commonly carried out by cybercriminals with the aim of taking user's personal information data by manipulating it. Facebook is a very popular social media platform in the world so it can be a wet place for phishing criminals. In this research, we built a Classification model to identify and prevent phishing attempts on Facebook posts. The dataset used in this study was obtained from Facebook user posts collected. Data processing is done by preprocessing the post text, including removing punctuation marks and words that are not important. The method used is Naïve Bayes to classify posts into phishing or not phishing categories. The Naïve Bayes method is used because of its ability to classify data with a good level of accuracy. This shows that the features selected in this study can be a strong indicator for detecting phishing on Facebook user posts. The results of the study show that Naïve Bayes can be an effective solution for phishing detection on Facebook user posts. In addition, the results of this research can provide valuable insight into the common characteristics of phishing posts on Facebook. With an accuracy value of 99.01%, it is hoped that this research can help increase awareness and security of Facebook users against phishing posts.
Klasifikasi Cuaca di DKI Jakarta Menggunakan Metode Gaussian Naive Bayes Raditama, Audi; Irwanto, Andhika Fariz; Majid, Yoga Fadillah Putra
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.55

Abstract

Weather Classification can help the community and related parties in making the right decisions, especially in planning daily activities. The Naïve Bayes method is used in this study because of its superiority in classifying data that has many attributes. The weather data used in this study includes variables such as humidity, degree Celsius temperature, weather. The naïve Bayes model is tested using test data for the accuracy of the model and evaluated by calculating the accuracy evaluation metric. Therefore, weather determination to obtain weather information needs to be made so that it can be utilized by the community. The results showed that the Gaussian Naïve Baiyes method could classify DKI Jakarta weather conditions with an accuracy of 81.25%. The results of this evaluation provide an overview of how good the Naïve Bayes model is in classifying DKI Jakarta weather
Strategi Promosi Menjaring Mahasiswa Baru Berdasakan Segmentasi Data PPMB Menggunakan K-Means tabaruk, zia; Hidayat, Sultan Bacharuddin Yusuf
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.57

Abstract

The number of universities that are spread out causes promotional strategies to greatly affect the number of new student admissions at any university including Bhayangkara University of Greater Jakarta. It is necessary to have the right strategy to be able to shape the characteristics and improve the quality of education at the University by understanding the right data segmentation to match the promotion target. This effort was carried out with the aim of determining a promotion strategy based on the location of residence, school origin, and parents' salary so that promotional activities run effectively every year. The data used in this study are data from the results of the new student admission process of Bhayangkara University Jakarta Raya in 2018-2022 which will then be processed. The data that has been managed will be processed through the KDD (Knowledge Discovery in Database) analysis technique [1] with 5 stages, namely selection, pre-processing, transformation, data mining, and evaluation, so as to produce a data mining analysis by considering the theory of the K-Means clustering algorithm as a research method.  
Analisis Tingkat Kepuasan Pengguna Apex Mobile Berdasarkan Rating Dan Ulasan Google Play Store Menggunakan Naïve Bayes fauzi, abdul rahman; Saputra , Ananda Hadi; Abdillah, Muhammad Hasbi
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.58

Abstract

Apex Mobile Game is one of the most popular games on the Google Play Store. To improve game quality and provide users with a better gaming experience, analyzing online satisfaction based on Google Play Store ratings is very important for game developers. The purpose of this study is to analyze user reviews of the Apex Mobile game on the Google Play Store to determine the positive or negative sentiment of users. the opinion analysis method used is Naïve Bayes. This research begins with data collection, data preprocessing, feature extraction, training the Naive Bayes model and testing the model. The data used in the study were 10,000 reviews of the Apex Mobile game obtained through reviews from the Google Play store. The results showed that the majority of users were satisfied with Apex Mobile. This can be seen from 70% of users expressing positive feelings. However, this study also found other indications that there are some issues that need to be resolved, such as Bugs and Game Limitations. The results of this study can be used by game developers to improve quality and provide users with a better gaming experience.  
Analisis Sentimen Menggunakan Algoritma Logistic Regression Pada Penerbangan Lion Air berdasarkan Ulasan Platform Online Rahmawati, Irma; Rika Fitriani, Tiara; No'eman, Achmad; Yusuf, Ajif Yunizar Pratama
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.60

Abstract

Perkembangan dunia digital telah mendorong masyarakat melakukan pemesanan tiket pesawat menggunakan platform online. Jenis transportasi ini sangat dibutuhkan oleh masyarakat untuk melakukan perjalanan baik antar provinsi maupun antar negara dalam waktu yang relatif singkat. Salah satu maskapai yang paling banyak diminati adalah Lion Air. Hal ini dikarenakan maskapai ini memiliki harga yang relative terjangkau dengan berbagai pilihan kelas penumpang. Namun masakapai penerbangan ini banyak menuai opini diberbagai media sosial sehingga mempegaruhi standar kualitas pelayanan pada maskapai tersebut. Tujuan penelitian ini untuk mengetahui opini positif, negatif dan netral terhadap pelayanan maskapai penerbangan Lion Air berdasarkan opini penumpang maskapai pada platform online. Metode Naïve Bayes, Random Forest dan Logistic Regression digunakan sebagai alat analisis untuk melihat persepsi terhadap maskapai tersebut. Hasil penelitian memperlihatkan tingkat akurasi klasifikasi opini penumpang maskapai penerbangan menggunakan Linear Regression sebesar 0.82 dengan nilai precission sebesar 0.82, recall 0.78, F1 – score 0.80 dan akurasinya 0.82. Sedangkan pada metode Naïve Bayes dan Random Forest masing – masing mendapatkan hasil akurasi 0.47 dan 0.39. Dari hal tersebut bahwa metode klasifikasi menggunakan Linear Regression menunjukkan hasil terbaik.

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