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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Faktor Exacta Jurnal Komtika (Komputasi dan Informatika) Jurnal Cendikia JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknologi Terpadu Jurnal ICT : Information Communication & Technology Competitive JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jitu: Jurnal Informatika Utama Competitive Journal of Computer Science Contributions (Jucosco) Journal of Informatics and Information Security
Prima Dina Atika
Fakultas Ilmu Komputer; Universitas Bhayangkara Jakarta Raya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Transportation Other

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Journal : Journal of Students‘ Research in Computer Science (JSRCS)

 1 
Sistem Informasi E-Repair Peralatan Elektronik Rumah Tangga Berbasis Android Retna Ayu Puspitasari; Prima Dina Atika; Tyastuti Sri Lestari
Journal of Students‘ Research in Computer Science Vol. 1 No. 1 (2020): Mei 2020
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jsrcs.v1i1.79

Abstract

Abstract E-repair aims to make it easier for consumers or the public who have difficulty finding repair workers in accordance with the required specifications. The research method used is a waterfall. Data obtained by giving questionnaires to the respondents and analyzed using the Likert Scale method. The results of this study are in the form of an Android-based E-Repair Information System for Household Electronic Equipment (A Case Study: CV. Buana Mitra Teknik) where in making the system using the Java programming language, Android studio software. The existence of an E-repair application becomes an electronic information media in order to facilitate the public to access it effectively and efficiently. Keywords: Information Systems, E-Repair, Waterfall. Abstrak E-repair bertujuan bertujuan untuk mempermudah konsumen atau masyarakat yang mengalami kesulitan dalam menemukan tenaga reparasi yang sesuai dengan spesifikasi yang dibutuhkan. Metode penelitian yang digunakan adalah waterfall. Data diperoleh dengan memberikan kuesioner pada para responden dan di analisis menggunakan metode Skala Likert. Hasil penelitian ini berupa aplikasi Sistem Informasi E-Repair Peralatan Elektronik Rumah Tangga Berbasis Android (Studi Kasus: CV. Buana Mitra Teknik) dimana dalam pembuatan sistem menggunakan bahasa pemrograman java, perangkat lunak android studio. Adanya aplikasi ¬e-repair menjadi media informasi elektronik agar mempermudah masyarakat mengaksesnya secara efektif dan efisien. Kata Kunci: Sistem Informasi, E-Repair, Waterfall.
Simulasi Management Network Menggunakan Metode VLAN Pada SMPN 255 Jakarta Gilby Lionska Wenas; Herlawati Herlawati; Prima Dina Atika
Journal of Students‘ Research in Computer Science Vol. 2 No. 1 (2021): Mei 2021
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jsrcs.v2i1.638

Abstract

Abstract The need for network technology cannot be avoided anymore because of its very pronounced benefits, where the network can help communication in sharing information and data by cutting time and distance. Internet is an indispensable need, especially in education, network quality, distribution or network segmentation, the hardware used, especially SMPN 255 Jakarta. The development method used by NDLC. Data collection is the interview method, observation, literature study, network network design using Star Topology, a network simulation system built using the media routerboard 750 hEX lite as the main router as well as a router for network distribution. It is hoped that this can help SMPN 255 Jakarta a little in distributing the existing local network according to the needs and increasing existing users, then providing convenience in network system maintenance or changes. The result is the successful configuration for implementing VLANs on the Jakarta 255 SMPN network to be able to distribute the network and use the existing features optimally so that it can develop the existing network but at a cost that is not that big. Keywords: Mikrotik, Network Design, Network Management, NDLC, VLAN. Abstrak Kebutuhan akan teknologi jaringan tidak dapat dihindari lagi karena manfaatnya yang sangat terasa, dimana jaringan dapat membantu komunikasi dalam pembagian informasi maupun data dengan memangkas waktu dan jarak. Internet merupakan suatu kebutuhan yang sangat diperlukan, terutama dalam pendidikan, kualitas jaringan, pendistribusian atau segmentasi jaringan, perangkat keras yang digunakan khususnya SMPN 255 Jakarta. Metode pengembangan yang digunakan NDLC. Pengumpulan data yaitu metode wawancara, observasi, studi pustaka, Perancangan jaringan jaringan menggunakan Topologi Star, sistem simulasi jaringan yang dibangun menggunakan media routerboard 750 hEX lite sebagai router utama sekaligus router untuk mendistribusi jaringan. Diharapkan ini bisa sedikit membantu SMPN 255 Jakarta dalam mendistribusikan jaringan lokal yang ada sesuai dengan kebutuhan dan peningkatan pengguna yang ada, kemudian memberikan kemudahan dalam pemeliharaan sistem jaringan ataupun perubahan. Hasil yang ada berupa berhasilnya konfigurasi untuk penerapan VLAN pada jaringan SMPN 255 Jakarta untuk dapat mendistribusikan jaringan dan penggunaan fitur yang ada dimanfaatkan dengan optimal sehingga dapat mengembangkan jaringan yang ada namun dengan biaya yang tidak begitu besar. Kata kunci: Mikrotik, Management Network, NDLC, Perancangan Jaringan, VLAN.
Sistem Informasi Pemilihan Peserta Program Indonesia Pintar (PIP) Dengan Metode K-Nearest Neighbor pada SD Negeri Pejuang V Kota Bekasi Sandy Satyo Prihatin; Prima Dina Atika; Herlawati Herlawati
Journal of Students‘ Research in Computer Science Vol. 2 No. 2 (2021): November 2021
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.087 KB) | DOI: 10.31599/jsrcs.v2i2.911

Abstract

The selection of participants for the Smart Indonesia Program (PIP) is an activity to determine students who are eligible for assistance. This study aims to create an information system for the Selection of Participants for the Smart Indonesia Program (PIP) which will assist Administrative Staff and SD Negeri Pejuang V Bekasi City in determining eligible and ineligible participants for assistance. The method used in this information system uses the K-Nearest Neighbor algorithm. The K-Nearest Neighbor process is carried out by giving weight to the student data attributes and looking for the Euclidean distance, then sorted from the smallest distance, after sorting the student data then looking for the closest distance to the training data. The K-Nearest Neighbor algorithm in data training is very fast, simple, easy to learn, effective with large training data and is resistant to data containing incorrect or anomalous values. The results of this study obtained student data as many as 77 students, there are True Positive (TP) data of 5 data, False Positive (FN) of 7 data, True Negative (TN) of 65 data and False Negative (FP) of 0. Results The accuracy obtained is 90.90% with a value of k=10.   Keywords: Information System, K-Nearest Neighbor, KNN, Program Indonesia Pintar (PIP).   Abstrak   Pemilihan peserta Program Indonesia Pintar (PIP) merupakan kegiatan menentukan siswa yang layak untuk mendapatkan bantuan. Penelitian ini bertujuan membuat sistem informasi untuk Pemilihan Peserta Program Indonesia Pintar (PIP) yang akan membantu Staff Administrasi dan pihak SD Negeri Pejuang V Kota Bekasi dalam menentukan peserta yang layak dan tidak layak untuk mendapat bantuan. Metode yang digunakan pada sistem informasi ini menggunakan algoritma K-Nearest Neighbor. Proses K-Nearest Neighbor ini dilakukan dengan memberikan bobot pada atribut data siswa dan mencari jarak Euclidean, selanjutnya diurutkan dari jarak yang terkecil, setelah diurutkan data siswa tersebut maka dicari jarak terdekat terhadap data training. Algoritma K-Nearest Neighbor dalam pelatihan data sangat cepat, sederhana, mudah dipelajari, efektif dengan data pelatihan besar serta tahan terhadap data berisi nilai yang salah atau anomali. Hasil dari penelitian ini data siswa yang didapat sebanyak 77 siswa, terdapat data True Positive (TP) sejumlah 5 data, False Positive (FP) sejumlah 7 data, True Negative (TN) sejumlah 65 data dan False Negative (FN) sejumlah 0. Hasil akurasi yang diperoleh mendapatkan nilai 90.90% dengan nilai k=10.   Kata kunci: K-Nearest Neighbor, KNN, Program Indonesia Pintar (PIP), Sistem Informasi.
Penentuan Pola Frekuensi Jenis Perawatan Kecantikan Berbasis Web Menggunakan Algoritma Apriori (Studi Kasus: Peterson Salon Bekasi) Herlawati Herlawati; Rahmadya Trias Handayanto; Sri Rejeki; Wowon Priatna; Prima Dina Atika; Syahbaniar Rofiah; Endang Retnoningsih; Faisal Adi Saputra; Galih Apriansha Pradana
Journal of Students‘ Research in Computer Science Vol. 3 No. 2 (2022): November 2022
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jsrcs.v3i2.1381

Abstract

Currently, technology can affect services in any field, both in areas such as salon services and sales of clothing products. How to plan a marketing strategy using the web based on service transaction data and products that are most often chosen by customers. Therefore, an information system for determining frequency patterns is needed using the website-based Apriori Algorithm method. The results of research on salons based on the type of beauty treatment obtained for the type of treatment with a minimum confidence = 70%, the first confidence value is 63% if the customer chooses to wash (shampoo), the customer chooses scissors, the second the confidence value is 100%, if the customer chooses to blow then chooses also cut, and the third confidence value is 86% if the customer chooses creambath then the customer chooses to cut too. Meanwhile at the shop clothes determining the frequency pattern of types of clothes with a minimum value of confidence = 70% so that the results include if a customer buys a veil, the customer buys a robe with a confidence value of 71.43% and if a customer buys khimar then the customer will also buy a robe with a confidence value of 78, 57%. With these results salon and clothing store owners can determine marketing strategies by providing the right product and service recommendations to customers. Keywords: Apriori Algorithm, Beauty Care, Recommendations, Sales   Abstrak Saat ini teknologi dapat mempengaruhi pelayanan dalam bidang apapun seperti jasa salon maupun penjualan produk pakaian. Bagaimana merencanakan strategi pemasaran menggunakan web berdasarkan data transaksi layanan dan produk yang paling sering dipilih oleh pelanggan. Oleh karena itu dibutuhkan sistem informasi penentuan pola frekuensi menggunakan metode Algoritma Apriori berbasis website. Hasil penelitian pada salon berdasarkan jenis perawatan kecantikan diperoleh untuk jenis perawatan dengan minimum confidence=70% yang pertama nilai confidence sebesar 63% jika pelanggan memilih cuci (keramas) maka pelanggan memilih gunting, yang kedua nilai confidence sebesar 100% jika pelanggan memilih blow maka memilih digunting juga, dan yang ketiga nilai confidence 86% jika pelanggan memilih creambath maka pelanggan memilih digunting juga. Sedangkan pada toko pakaian penentuan pola frekuensi jenis baju dengan nilai minimum confidence= 70% sehingga mendapatkan hasil diantaranya jika pelanggan membeli kerudung maka pelanggan membeli gamis dengan nilai confidence sebesar 71,43%  dan  jika  pelanggan  membeli  khimar maka pelanggan juga akan membeli gamis dengan nilai confidence sebesar 78,57%. Dengan hasil tersebut pemilik salon dan toko pakaian dapat menentukan strategi pemasaran dengan memberikan rekomendasi jasa dan produk yang tepat kepada pelanggan. Kata kunci: Algoritma Apriori, Penjualan, Perawatan Kecantikan, Rekomendasi
Klasifikasi Keuntungan pada Bengkel AS Motor BMW Menggunakan Metode Algoritma C4.5 Julianto; Prima Dina Atika; Joni Warta
Journal of Students‘ Research in Computer Science Vol. 4 No. 1 (2023): Mei 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jsrcs.v4i1.2558

Abstract

The growth of the motor vehicle and car repair services business is a business that has very high growth in Indonesia, due to the large number of motorized vehicles and cars. Profits in business are important because profits determine whether a company is progressing or not. The method used in this research is Classification Data Mining using the C4.5 Algorithm method, with the attributes involved namely income, cash, spare parts payable, water, electricity, employee salaries, rental costs, consumables, incidental costs, and net income. Algorithm C4.5, which is an algorithm for changing the form of data (tables) into a tree model and then changing the tree model into a rule. The application of the C4.5 algorithm to the profit and loss classification system at workshops was successfully carried out with an accuracy rate of 72.73% from the total data calculation results, and has been implemented in the form of a profit and loss classification application system for website-based workshops. The classification system of advantages and disadvantages in workshops as a result of the research that has been done can be used as a recommendation.  Keywords: Algorithm C4.5, Data Mining, Profit Classification   Abstrak Pertumbuhan bisnis jasa perbaikan kendaraan bermotor dan bermobil merupakan bisnis memiliki pertumbuhan sangat tinggi di Indonesi, dikarenakan banyaknya kendaraan bermotor dan bermobil. Keuntungan dalam bisnis merupakan hal penting dikarenakan keuntungan menentukan perusahaan mengalami kemajuan atau tidak. Adapun metode yang digunakan dalam penelitian  yaitu Data Mining Klasifikasi menggunakan metode Algoritma C4.5, dengan atribut yang terlibat yaitu pendapatan, kas, utang sparepart, air, listrik, gaji karyawan, biaya sewa, bahan habis pakai, biaya tak terduga, dan pendapatan bersih. Algoritma C4.5, yaitu algoritma untuk mengubah bentuk data (tabel) menjadi model pohon kemudian mengubah model pohon tersebut menjadi rule. Penerapan algoritma C4.5 pada sistem klasifikasi keuntungan dan kerugian pada bengkel berhasil dijalankan dengan menghasilkan tingkat akurasi sebanyak 72,73% dari hasil perhitungan data total, dan telah diimplementasikan dalam bentuk sistem aplikasi klasifikasi keuntungan dan kerugian pada bengkel berbasis website. Sistem klasifikasi keuntungan dan kerugian pada bengkel sebagai hasil dari penelitian yang telah dilakukan dapat dijadikan rekomendasi. Kata kunci: Data Mining, Algoritma C4.5, Klasifikasi Keuntungan
Sistem Informasi Pemilihan Peserta Program Indonesia Pintar (PIP) Dengan Metode K-Nearest Neighbor pada SD Negeri Pejuang V Kota Bekasi Prihatin, Sandy Satyo; Atika, Prima Dina; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 2 No. 2 (2021): November 2021
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/g0q7c702

Abstract

The selection of participants for the Smart Indonesia Program (PIP) is an activity to determine students who are eligible for assistance. This study aims to create an information system for the Selection of Participants for the Smart Indonesia Program (PIP) which will assist Administrative Staff and SD Negeri Pejuang V Bekasi City in determining eligible and ineligible participants for assistance. The method used in this information system uses the K-Nearest Neighbor algorithm. The K-Nearest Neighbor process is carried out by giving weight to the student data attributes and looking for the Euclidean distance, then sorted from the smallest distance, after sorting the student data then looking for the closest distance to the training data. The K-Nearest Neighbor algorithm in data training is very fast, simple, easy to learn, effective with large training data and is resistant to data containing incorrect or anomalous values. The results of this study obtained student data as many as 77 students, there are True Positive (TP) data of 5 data, False Positive (FN) of 7 data, True Negative (TN) of 65 data and False Negative (FP) of 0. Results The accuracy obtained is 90.90% with a value of k=10.
Pencarian Jalur Terdekat Pada Pemetaan Sekolah Dasar Dengan Algoritma A-Star (A*) Berbasis Web Tambun, Jerisman Jhon Wesli; Herlawati, Herlawati; Atika, Prima Dina
Journal of Students‘ Research in Computer Science Vol. 3 No. 1 (2022): Mei 2022
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/8e7aw911

Abstract

Elementary school is the starting place for a child to start education and get various kinds of experiences. Elementary school is the highest level of education compared to Junior High School and Senior High School. The large number of elementary schools makes parents/guardians not know for sure where the elementary school is located. This makes parents/guardians not have many references to send their children to school in the future. Based on the existing problems, the author proposes a web-based geographic information system that uses the A-Star Algorithm (A*) to be used as a means of information and also to add references to parents/guardians. The A-Star Algorithm (A*) is a method to search for information about the distance to reach the destination by selecting the closest route. The result of this research is a web-based geographic information system that can provide information about 6 samples of elementary schools along with the location and the closest route that can be passed in the Mustikajaya District area.
Analisis Sentimen Masyarakat Terhadap Perkuliahan Daring di Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine Samuel, Federick Dedi; Atika, Prima Dina; Setiawati, Siti
Journal of Students‘ Research in Computer Science Vol. 4 No. 2 (2023): November 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/6691v571

Abstract

The COVID-19 pandemic has changed the education landscape around the world, resulting in the cessation of in-person teaching and learning activities and encouraging the adoption of online learning systems. Many Indonesians express their opinions and thoughts about online courses through the social media Twitter. Therefore, this study aims to analyze people's sentiment towards online lectures on Twitter using Naïve Bayes and Support Vector Machine (SVM) methods. Data for sentiment analysis is taken from Twitter using the keywords "#college", "#daring", and "#kuliahdaring". This study limits data collection to the range of 2021-2022. A total of 1,260 Tweets were analyzed, with 633 Tweets having positive sentiments and 627 Tweets having negative sentiments. This study uses Naïve Bayes and Support Vector Machine algorithms to classify positive and negative sentiments in Tweets. The results showed that Naïve Bayes algorithm achieved the highest accuracy of 72%, while Support Vector Machine achieved 66% accuracy.
Analisis Clustering K-Means untuk Pemetaan Tingkat Pengangguran Terbuka di Provinsi-Provinsi Indonesia Tahun 2013-2023 Ramadhan, Alif Izzuddin; Ramdhania, Khairunnisa Fadhilla; atika, prima dina
Journal of Students‘ Research in Computer Science Vol. 5 No. 2 (2024): November 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/wbpydb62

Abstract

This study analyzes unemployment rates in Indonesian provinces using data from the Central Statistics Agency (BPS) for the period 2013-2023 and the K-Means clustering algorithm. The aim is to group regions based on the Open Unemployment Rate (TPT). Two main clusters were produced: one with a high unemployment rate (cluster 0) and one with a low unemployment rate (cluster 1). Cluster 0 consists of 12 provinces, while cluster 1 consists of 22 provinces. The model evaluation shows a Davies-Bouldin Index score of 0.7041, indicating good clustering quality. The clustering results are visualized in the form of a map for easy interpretation. This research is expected to help policymakers design more effective policies in reducing unemployment in Indonesia, provide deep insights into regional differences in terms of unemployment, and support targeted decision-making.
Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM) AlHakim, Abdu Malik; Atika, Prima Dina; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/96sfw544

Abstract

The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Co-Authors .S.T., M.Kom., Suhadi Afzil Ramadian Ahmad Fathurrozi Ahmad Fathurrozi Aida Fitriyani Ajif Yunizar Pratama Yusuf AlHakim, Abdu Malik Andy Achmad Hendharsetiawan Anita Setyowati Srie Gunarti Beno Aditya Sanusi Dadan Irwan Dani Yusuf Endang Retnoningsih Endang Retnoningsih Faisal Adi Saputra Fata Nidaul Khasanah Fitria Nurapriani Galih Apriansha Pradana Gedhe Hilman Wakhid Gilby Lionska Wenas Hadi Kusmara Haryono Haryono Herlawati Herlawati Indah Dwijayanthi Nirmala Inna Ekawati Ira Wardani Ismaniah Ismaniah Joni Warta Julianto Lestari, Panca Indah Maimunah Malikus Sumadyo Mamang Jhulianawati Mugiarso Mugiarso Muhamad Galih Muhammad Riky Sudrajat Mukhlis Mukhlis Nur Amanda Pratiwi Priatna , Wowon Prihatin, Sandy Satyo Rafika Sari RAFIKA SARI Rahmadya Trias Handayanto Ramadhan, Alif Izzuddin Ramdhania, Khairunnisa Fadhilla Rasim Rasim Rasim, Rasim Resty Nandya Retna Ayu Puspitasari Retno Nugroho Whidhiasih Rizky Alfiansyah Robertoi . Samuel, Federick Dedi Sandy Satyo Prihatin Sari , Rafika Seta Samsiana Seta Samsiana Seta Samsiana Shofa Shofia SITI SETIAWATI Sri Rejeki Sri Rejeki Sugeng Murdowo Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Syahbaniar Rofiah Tambun, Jerisman Jhon Wesli Tyastuti Sri Lestari Tyastuti Sri Lestari Wijati, Damar