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All Journal International Journal of Electrical and Computer Engineering Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Jurnal Sains dan Informatika Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation Information Technology and Application (JINITA) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Pengabdian Kepada Masyarakat (JPKM) Langit Biru
Maharrani, Ratih Hafsarah
Politeknik Negeri Cilacap
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Transportation

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SIPGANG: Sistem Pendukung Keputusan Rekomendasi Magang Industri Berbasis Multi Attribute Utility Theory (MAUT) Maharrani, Ratih Hafsarah; Supriyono, Abdul Rohman; Syafirullah, Lutfi
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 7, No 3 (2021): Volume 7 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v7i3.49478

Abstract

Magang adalah proses penerapan bidang keilmuan dan keterampilan yang diperoleh di kampus pada dunia industry. Dalam pelaksanaannya, saat mahasiswa selesai melaksanakan magang ada beberapa mengeluhkan bahwa perusahaan tempat magang tidak sesuai dengan standar yang diinginkan, tidak sesuai bidang serta beban kerja yang berlebih sehingga dalam pelaksanaanya mahasiswa merasa tidak bisa optimal dalam menggunakan kemampuan yang dimiliki. Hal ini dikarenakan adanya subjektifitas dalam penentuan tempat magang, mahasiswa memilih sendiri tempat magang tersebut yang terkadang belum mengetahui job desk yang akan dikerjakan. Selain itu koordinator magang jurusan merekomendasikan tempat magang berdasarkan penilaian pembimbing magang saat visitasi padahal tidak mengetahui keadaan sebenarnya yang telah terlaksana. Sehingga dalam hal ini dibutuhkan adanya sebuah sistem yang mampu membantu dalam pengambilan keputusan rekomendasi tempat magang. Penelitian ini dibuat dengan menerapkan metode pengembangan system Rapid Development Prototyping (RAD) dan penilaian rekomendasi diperoleh menggunakan metode MAUT (Multi Attribute Utility Theory). Metode MAUT akan mengolah penilaian dari  masing kriteria (jam kerja, bobot tugas yang diberikan selama magang, kesesuaian tugas dengan keahlian, standar perusahaan, penerapan K3 di perusahaan, fasilitas dan peralatan praktik untuk peserta magang serta bidang keahlian) sesuai dengan bobot yang ditentukan dengan tujuan memberikan penilaian dari sisi mahasiswa yang telah selesai pelaksanaan magang terhadap industri bersangkutan. Pengujian pada aplikasi SIPGANG penentuan rekomendasi industry menggunakan uji kuisioner dengan Sistem Usability Scale (SUS) dan didapatkan hasil akhir 72 yang menyatakan bahwa system dalam kategori layak untuk digunakan. 
Sentiment review of coastal assessment using neural network and naïve Bayes Somantri, Oman; Purwaningrum, Santi; Maharrani, Ratih Hafsarah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp681-689

Abstract

An assessment of a place will provide an overview for other people whether the place is feasible to be visited or not. Assessment of coastal places will provide a separate assessment for potential visitors in considering visitation. This article proposes a model using the neural network (NN) and naïve Bayes (NB) methods to classify sentiment toward coastal assessments. The proposed NN and NB models are optimized using information gain (IG) and feature weights, namely particle swarm optimization (PSO) and genetic algorithm (GA) which are carried out to increase the level of classification accuracy. Based on the experimental results, the best level of accuracy for the classification of coastal assessments is 87.11% and is named the NB IG+PSO model. The best model obtained is a model that can be used as a decision support for potential beach visitors in deciding to visit the place.
Pendampingan Pengelolaan Manajemen Penerbitan Jurnal Ilmiah Sesuai Standar Akreditasi Somantri, Oman; Hafsarah Maharrani, Ratih
Jurnal Pengabdian Kepada Masyarakat Vol 3 No 02 (2022): Jurnal Pengabdian Kepada Masyarakat (JPKM) Langit Biru
Publisher : Politeknik Penerbangan Indonesia Curug

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54147/jpkm.v3i02.738

Abstract

Publikasi ilmiah dalam bentuk artikel ilmiah merupakan sebuah kewajiban bagi seorang peneliti dan tentunya jurnal yang dituju harus memiliki kredibilitas dan kualitas yag baik. Upaya peningkatan kualitas penerbit dalam mengelola sebuah jurnal ilmiah sesuai dengan standar menjadi tuntutan dan permasalahan yang dialami oleh penerbit khususnya penerbit Publikasi Indonesia Cirebon. Kegiatan PKM ini diusulkan dalam bentuk pendampingan sebagai upaya peningkatan pengetahuan dalam mengelola jurnal ilmiah sesuai dengan standar. Tahapan kegiatan dilakukan melalui tahapan perencanaan, pelaksanaan kegiatan menggunakan metode ceramah dan diskusi, serta tahapan terakhir adalah evaluasi kegiatan. Hasil kegiatan memberikan peningkatan pengetahuan dan keterampilan para pengelola jurnal terutama untuk persiapan dalam standarisasi manajemen pengelolaan jurnal yang mengarah kepada akreditasi jurnal. Kegiatan pendampingan yang dilaksanakan sangat penting karena dapat memberikan sebuah peningkatan pengetahuan dalam pengelolaan jurnal sesuai dengan permasalahan yang dihadapi oleh mitra PKM.
Metode Simple Multi Attribute Rating Technique Method (SMART) dalam Penentuan Uang Kuliah Tunggal Mahasiswa Nur’Aini, Ummu Habibah; Maharrani, Ratih Hafsarah; Prihantara, Andesita; Purwanto, Riyadi
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.764

Abstract

Uang Kuliah Tunggal (UKT) adalah sebuah sistem pengenaan biaya pendidikan yang diterapkan di beberapa perguruan tinggi di Indonesia. UKT bertujuan untuk mengakomodasi perbedaan kemampuan ekonomi mahasiswa dan keluarganya dalam membayar biaya Pendidikan. Dalam sistem UKT, biaya pendidikan yang harus dibayarkan oleh mahasiswa ditentukan berdasarkan kemampuan ekonomi keluarga dan tingkat pendapatan mereka. Selama ini, proses penentuan UKT masih belum konsisten dan seringkali berubah acuannya. Selain itu penentuannya pun membutuhkan waktu yang cukup lama karena terdapat beberapa point yang memerlukan penilaian berdasar kondisi ekonomi dari mahasiswa. Perguruan tinggi akan menentukan kategori UKT yang berbeda dengan besaran biaya pendidikan yang berbeda pula. Biasanya terdapat beberapa kategori UKT, misalnya UKT 0 (untuk keluarga dengan kemampuan ekonomi terbatas), UKT 1, UKT 2, UKT 3, dan seterusnya. Mahasiswa dan keluarganya akan diberikan UKT sesuai dengan kategori yang telah ditentukan. Metode SMART (Simple Multi Atribute Rating Technique) diimplementasikan pada penelitian ini dalam proses penentuan Uang Kuliah Tunggal Mahasiswa. Kerangka kerja yang terstruktur dan sistematis diberikan oleh metode SMART untuk membandingkan alternatif berdasarkan kriteria yang relevan. Beberapa kriteria yang digunakan dalam penelitian yakni jumlah asset, tanggungan, jarak rumah, jumlah penghasilan orang tua, daya listrik, sumber dari air rumah tangga, jenis pekerjaan bagi ayah dan ibu. Penelitian menghasilkan nilai peringkat uang kuliah tunggal dengan 8 golongan UKT yang ditentukan berdasarkan dari parameter yang telah ditentukan sebelumnya.
Handling missing values and clustering industrial liquid waste using K-medoids Maharrani, Ratih Hafsarah; Abda'u, Prih Diantono; Ikhtiagung, Ganjar Ndaru; Rahadi, Nur Wahyu; Zaenurrohman, Zaenurrohman
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1411-1420

Abstract

The textile industry is a significant contributor to environmental pollution due to its wastewater, which contains hazardous substances such as dyes, heavy metals, and chemicals that can severely harm aquatic ecosystems. Effective management of this wastewater is crucial to mitigate its environmental impact. This study focuses on classifying industrial liquid waste data using the K-medoids clustering method, chosen for its robustness to noise and outliers compared to K-means. To address challenges in wastewater data processing, such as missing values and varying data scales, two approaches are compared: replacing missing values with zero and K-nearest neighbors (KNN) imputation, alongside Z-score normalization for data uniformity. The clustering quality is evaluated using the Davies-Bouldin index (DBI) for cluster variations of k=2, 3, 4, and 5. The results show that the best clustering quality is achieved at k=2, with the smallest DBI values obtained using KNN imputation (0.139) and zero replacement (0.149). The superior performance of KNN imputation highlights its effectiveness in handling missing data. These findings provide valuable insights into the characteristics of textile industry wastewater pollution, offering a robust framework for effective wastewater management. The study concludes with practical recommendations for policymakers and industry stakeholders to adopt advanced data-driven approaches for sustainable wastewater treatment strategies.
Optimisation of Criminal Data Clustering Model using Information Gain Diantono Abda’u, Prih; Maharrani, Ratih Hafsarah; Nur Faiz, Muhammad; Somantri, Oman
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2741

Abstract

Crime is a phenomenon that significantly impacts society, necessitating mapping efforts that can be utilized for further analysis. Clustering, as a data analysis technique, groups objects based on similarities or differences in their characteristics. This approach enhances the understanding of data by identifying patterns and relationships between criminal events, such as crime type, time, and location. By clustering crime data based on similar characteristics, authorities can make more effective and efficient decisions in crime prevention and control. However, selecting too many attributes can negatively affect clustering performance. To address this issue, this study applies Information Gain reduction to reduce data dimensionality by eliminating attributes with low informational contribution. Additionally, three clustering methods K-Medoid, K-Means, and X-Means are compared to evaluate their performance. The concept of Information Gain is also integrated to optimize cluster formation, measuring how much an attribute contributes to distinguishing objects within a cluster. By leveraging Information Gain, this study aims to identify the most relevant and influential attributes in forming clusters that accurately represent crime data characteristics. Furthermore, the number of clusters generated is evaluated using the Davies-Bouldin Index (DBI). The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.
Classification of DDoS Attacks based on Network Traffic Patterns Using the k-Nearest Neighbor (k-NN) Algorithm Faiz, Muhammad Nur; Maharrani, Ratih Hafsarah; Sari, Laura; Muhammad, Arif Wirawan; Supriyono, Abdul Rohman
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1834

Abstract

Many server attacks disrupt industrial or business operations. Attacks that flood bandwidth with simultaneous requests can overwhelm a system, leading to significant downtime and financial losses. Additionally, breaches that compromise sensitive data can damage a company's reputation and erode customer trust. DDoS attacks, or Distributed Denial of Service attacks, are among the most common types of server attacks. DDoS has been proven to cause server downtime, and one effective way to mitigate this attack is to detect and classify it using a machine learning approach. The K-Nearest Neighbor (KNN) algorithm, a simple yet effective classification method based on similarity measures, is known for its high accuracy. The current research builds upon two stages: the feature extraction stage and the classification stage, with the ultimate goal of improving the accuracy of DDoS identification using the CICDDoS2019 dataset. Based on this premise, the detection accuracy can be improved by enhancing these two stages. At a value of k equal to 3, this study produces an accuracy of 99.73%.
Penerapan Metode Simple Additive Weighting (SAW) dan Tabel Keputusan pada Sistem Pendukung Keputusan Menentukan Tingkat Punishment Siswa Bermasalah Purwanto, Riyadi; Novia Prasetyanti, Dwi; Hafsarah Maharrani, Ratih; Syafirullah, Lutfi
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.780

Abstract

RETRACTED Following a rigorous, carefully concerns and considered review of the article published in Infotekmesin to article entitled “Penerapan Metode Simple Additive Weighting (SAW) dan Tabel Keputusan pada Sistem Pendukung Keputusan Menentukan Tingkat Punishment Siswa Bermasalah ” Vol 12, No 2, pp.115-121, July 2021, DOI: https://doi.org/10.35970/infotekmesin.v12i2.780 This paper has been retracted at the request of the author of this article because by mistake the same article has been published in another journal publisher. The article contained redundant material, the paper published in Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science - iCAST-ES, ISBN 978-989-758-615-6; ISSN 2975-8246, pages 1194-1202. DOI: 10.5220/0010962300003260, The document and its content has been removed from Infotekmesin, and reasonable effort should be made to remove all references to this article.
Penerapan Metode First Come First Served (FCFS) Pada Sistem Informasi Layanan Perawatan dan Perbaikan Aset Kampus Riyadi Purwanto; Linda Perdana Wanti; Hafsarah Maharrani, Ratih; Rostika Listyaningrum
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1548

Abstract

Assets are resources owned by an entity. Cilacap State Polytechnic is a higher education institution that has many assets. Current asset maintenance and repair services are still carried out conventionally. Each work unit must fill out a maintenance and repair proposal form and then submit it to the Maintenance, Repair, and Maintenance Unit (UP3). The proposal will be recorded and scheduled to be followed up by the Technician. The problem that occurs is that the proposal form is often scattered and even lost so the response time is often slow. In addition, maintenance and repair schedules are often not ordered according to the time of the request. This has an impact on the abandonment of work in certain work units. The purpose of this research is to create an information system for the repair and maintenance of campus assets. In order for maintenance and repair services to be carried out according to the time of the request, the FCFS method is applied. Thus, the service process for maintaining and repairing campus assets becomes more organized, schedules (queues) are sequenced, and the response time for incident handling is faster.
Coastal Sentiment Review Using Naïve Bayes with Feature Selection Genetic Algorithm Somantri, Oman; Maharrani, Ratih Hafsarah; Purwaningrum, Santi
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.43988

Abstract

Purpose: The tourism potential in the maritime sector can be Indonesia's mainstay at this time, especially in enjoying the charm of the natural beauty of the coast as people know Indonesia is an archipelagic country. The purpose of this study is to find the best model by applying the feature selection genetic algorithm (GA) and Information Gain (IG) to get the best Naïve Bayes (NB) model and the best features to produce the best level of sentiment classification accuracy.Methods: The stages of the research were carried out by going through the process of searching, pre-processing, analyzing research data using the Naïve Bayes model and optimizing genetic algorithms, validating data, and model evaluation.Result: The experimental results show that the best model is naïve Bayes based on information gain and the genetic algorithm yields an accuracy rate of 86.34%.Novelty: The main contribution to this research is proposing a new model of the best NB optimization model by applying an optimization algorithm in the search for feature selection to increase sentiment classification accuracy.
Co-Authors Abda'u, Prih Diantono Arif Wirawan Muhammad Diantono Abda’u, Prih Ikhtiagung, Ganjar Ndaru Linda Perdana Wanti Lutfi Syafirullah Muhammad Nur Faiz, Muhammad Nur Novia Prasetyanti, Dwi Nur Faiz, Muhammad Nur’Aini, Ummu Habibah Oman Somantri Prihantara, Andesita Purwaningrum, Santi Rahadi, Nur Wahyu Riyadi Purwanto Rostika Listyaningrum Santi Purwaningrum Sari, Laura Supriyono, Abdul Rohman Zaenurrohman, Zaenurrohman