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All Journal JURTEKSI Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Media Informatika International Journal of Public Health Excellence (IJPHE) Internet of Things and Artificial Intelligence Journal International Journal of Community Service (IJCS) Jurnal Manajemen Informatika dan Bisnis Digital International Journal of Health, Engineering and Technology Journal of Innovative and Creativity Sevaka: Hasil Kegiatan Layanan Masyarakat
Riszki Fadillah
Institut Teknologi Dan Kesehatan Ika Bina
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Selection of Head of Study Program using Weighted Aggregated Sum Product Assessment (WASPAS) method Ramadani, Ramadani; Fadillah, Riszki; Fitriyani, Intan Nur
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.803

Abstract

Selecting a Head of Study Program is a crucial strategic decision in education, particularly in Vocational High Schools. At the Software Engineering Study Program Vocational School Sitibanun Sigambal, Labuhanbatu, Rantau Prapat, this process becomes highly complex due to the involvement of various criteria, such as Psychotest Scores, IQ Tests, Communication Skills, Cognitive Tests, and Teaching Experience. The Weighted Aggregated Sum Product Assessment (WASPAS) method, which combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM), is utilized to enhance the accuracy and efficiency of decision-making. This method enables a more objective and structured selection process by leveraging information technology. Based on implementing the Decision Support System (DSS) using the WASPAS method, it can be concluded that it is highly effective in determining the best Head of Study Program rankings, considering the complex criteria and the need for accurate decisions. This DSS facilitates the selection process with results that are more objective, transparent, and aligned with the School's needs and priorities, thus aiding in achieving the School's mission of providing high-quality education.
Addict Coffee Barista Recruitment Decision Support System Using the ARAS Method Mesran, Mesran; Fadillah, Riszki; Wahyu, Riski Ferita
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6249

Abstract

Barista is a person who works in a coffee shop as a delicious coffee maker. So it is necessary to recruit baristas who can work in coffee shops and have responsibilities that not only mix coffee but also have skills in processing coffee beans. The problem in the barista recruitment process is the process of determining barista candidates who are only selected individually so that it is less accurate to get barista candidates who have the expected skills so that it can have an impact on opinions on the coffee shop. So the solution is provided through a decision support system, a highly interactive computer-based system that assists in making a decision to utilise data and models in solving unstructured and semi-structured problems. The method used in making these decisions is the Additive Ratio Assessment Method (ARAS). A total of 11 people who will become data samples and five criteria are used as rules for assessing (selecting). The ARAS method is able to provide maximum results to obtain superior barista recruitment with a result of 5,342, namely A1 as the selected alternative in barista recruitment after going through the method application stage.
Edukasi Tentang Pemanfaatan Internet dan Teknologi Internet Of Things (IoT) di Kelurahan Padang Matinggi, Kecamatan Rantau Utara Riszki Fadillah; Intan Nur Fitriyani
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 3 No. 1 (2025): Februari : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v3i1.311

Abstract

The utilization of internet technology and the Internet of Things (IoT) has become an integral part of various aspects of modern life, including the development of Community Social Worker (PSM) cadres' capacity. This study aims to provide education on the use of the internet and IoT to PSM cadres in Padang Matinggi Village, Rantau Utara Subdistrict, so they can optimize these technologies in supporting their social work activities. This community service activity is carried out through counseling and training that covers the basics of internet usage, the introduction of IoT concepts, and their application in social data management and community activities. The results of this activity showed a significant improvement in the participants' understanding of the technology provided, measured through pre-test and post-test evaluations. With a better understanding of technology, it is expected that PSM cadres can be more effective in performing their duties and contribute to improving the welfare of the community in Padang Matinggi Village.
Penerapan Metode K-Means Clustering untuk Klasifikasi Efek Samping Penggunaan Obat ARV pada Pasien HIV di Puskesmas Fadillah, Riszki; Fitriyani, Intan Nur
Jurnal Media Informatika Vol. 6 No. 1 (2024): Jurnal Media Informatika Edisi September - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pola efek samping yang dialami pasien HIV yang menjalani terapi antiretroviral (ARV) menggunakan metode K-Means Clustering. Data yang digunakan berasal dari rekam medis pasien di puskesmas, yang mencakup informasi tentang usia pasien, jenis efek samping, durasi terapi ARV, dan pola penggunaan obat ARV. Metode Elbow dan Silhouette Score digunakan untuk menentukan jumlah cluster optimal, yang menghasilkan tiga cluster dengan tingkat pemisahan yang baik. Cluster pertama mencakup pasien dengan efek samping ringan dan durasi terapi pendek (kurang dari 6 bulan), cluster kedua berisi pasien dengan efek samping sedang dan durasi terapi menengah (6-12 bulan), sementara cluster ketiga meliputi pasien dengan efek samping berat dan durasi terapi lebih panjang (>12 bulan). Hasil clustering ini memberikan wawasan penting untuk perencanaan intervensi medis yang lebih tepat sasaran, seperti pemantauan rutin untuk cluster 1, pendekatan khusus untuk cluster 2, dan perhatian medis intensif untuk cluster 3. Visualisasi data dengan scatter plot mengilustrasikan hubungan antara keparahan efek samping dan durasi terapi, memudahkan pemahaman tentang pola distribusi pasien yang mengalami efek samping ARV. Temuan ini diharapkan dapat meningkatkan kualitas perawatan dan kepatuhan pasien terhadap terapi ARV.
Penerapan Naive Bayes untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan pada Program Studi Kebidanan Sirait, Fahruzi; Sakti Tanjung, Rani Darma; Tusakdiyah Harahap, Halimah; Fadillah, Riszki
Jurnal Media Informatika Vol. 6 No. 1 (2024): Jurnal Media Informatika Edisi September - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini berfokus pada pemantauan perkembangan anak, yang merupakan aspek penting dalam kesehatan anak, terutama pada masa emas (golden period) perkembangan. Keterlambatan perkembangan anak sering kali tidak terdeteksi secara dini, yang dapat berdampak negatif pada kualitas hidup mereka di masa depan. Penelitian ini bertujuan untuk mengeksplorasi penerapan metode Naive Bayes dalam mengidentifikasi keterlambatan perkembangan anak berdasarkan data kesehatan yang tersedia. Dengan menggunakan pendekatan kuantitatif dan eksperimen, penelitian ini menganalisis data dari rekam medis, hasil pemeriksaan kebidanan, serta informasi tambahan dari orang tua. Metode Naive Bayes dipilih karena kemampuannya dalam mengolah data besar dan memberikan klasifikasi yang akurat dengan cepat. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes dapat digunakan untuk mengklasifikasikan status perkembangan anak ke dalam kategori normal atau terlambat dengan tingkat akurasi yang tinggi. Dengan memanfaatkan sistem informasi kesehatan, tenaga medis dapat lebih mudah mengakses dan menganalisis data kesehatan anak, sehingga memungkinkan deteksi dini terhadap keterlambatan perkembangan. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan efektivitas pemantauan kesehatan anak dan mendukung intervensi yang tepat waktu. Selain itu, temuan ini juga membuka peluang untuk pengembangan lebih lanjut dalam penerapan teknologi informasi di bidang kebidanan dan kesehatan anak, dengan fokus pada peningkatan kualitas layanan kesehatan secara keseluruhan
Sentiment Analysis on Twitter Social Media towards Najwa Shihab Using Naïve Bayes Algorithm and Support Vector Machine (SVM) Fahruzi Sirait; Desi Irpan; Riszki Fadillah; Rizalina Rizalina; Riswan Syahputra Damanik
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.280

Abstract

With the rapid growth of digital technology, social media has become a key platform for sharing information and opinions. Twitter, one of the most popular platforms in Indonesia, enables users to interact directly with public figures such as Najwa Shihab. This study aims to analyze public sentiment toward Najwa Shihab on Twitter using sentiment analysis, specifically employing the Naïve Bayes and Support Vector Machine (SVM) algorithms. Sentiment analysis is essential to understanding public opinion, as it classifies text into categories like positive, negative, or neutral, providing valuable insights into societal perspectives on public figures. In this study, 10,000 tweets related to Najwa Shihab were collected from January 1, 2023, to January 31, 2023. Data preprocessing steps such as data cleaning, tokenization, stopwords removal, and filtering were conducted to ensure high-quality data for analysis. The Naïve Bayes and SVM algorithms were applied using RapidMiner to classify the sentiment of the tweets. The performance of both algorithms was evaluated based on accuracy, precision, recall, and F1-score.The results revealed that SVM outperformed Naïve Bayes in all metrics, demonstrating its superior ability to classify sentiments correctly. The sentiment distribution indicated a majority of positive opinions toward Najwa Shihab, with fluctuations in negative sentiment during specific events. This study provides insights into public sentiment analysis and contributes to understanding social media opinions on public figures.
Analysis of Factors Causing Students' Failure to Complete Their Thesis on Time Using the Random Forest Algorithm Riszki Fadillah; Intan Nur Fitriyani; Nur Indah Nasution; Rahadatul 'Aisy Riadi; Dinda Salsabila Ritonga
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.281

Abstract

This research aims to analyze the factors that influence students' delays in completing final assignments using the Random Forest algorithm. The data used includes variables such as GPA, number of credits, employment status, frequency of guidance, organizational activities, and personal motivation. These variables were analyzed to determine their effect on students' ability to complete their final assignments on time. The Random Forest model is applied to predict whether students complete their final assignments on time or not. The model results show an accuracy of 63.33%, with the frequency of guidance and personal motivation being the most influential factors in completing the final assignment on time. Followed by the number of credits and GPA, which also have a significant but smaller influence. Organizational activity factors and employment status have a lower contribution to tardiness, but are still relevant in the context of student time management. Based on these results, research suggests the importance of academic guidance support and motivation management to help students overcome obstacles in completing their final assignments on time. This research, which uses the case of ITKES Ika Bina students, is expected to provide recommendations for universities in improving the academic mentoring process to support student graduation.
Implementation of Password Validation using a Combination of Letters, Numbers and Symbols in the New Student Registration Application Sentosa Pohan; Putri Ramadani; Riszki Fadillah; Yusril Iza Mahendra Hasibuan; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.282

Abstract

This research aims to evaluate the implementation of password validation using a combination of letters, numbers and symbols in new student registration applications in increasing the level of application security. This research method involves implementing a password validation system with strict criteria, as well as testing password strength using brute force attacks. The test results show that passwords that meet the criteria take time 150 seconds to be broken using brute force, while passwords that only use letters only take time 10 seconds. Surveys of users show that 70% feel comfortable with this validation system, though 40% find it difficult to create a valid password. As much 85% users consider this system to improve application security. This research suggests that new student registration applications adopt a strict password validation system to increase the protection of users' personal data, while providing solutions for users to create more secure passwords.complex but easy to remember. The implementation of this system is expected to strengthen application security and increase user confidence in the protection of their personal data.
Perbandingan Algoritma Naïve Bayes, C4.5, dan K-Nearest Neighbor untuk Klasifikasi Kelayakan Program Keluarga Harapan Ramadani, Putri; Fadillah, Riszki; Adawiyah, Quratih; Suerni, Suerni; Al Ghazali, Baginda Restu
Jurnal Media Informatika Vol. 6 No. 1 (2024): Jurnal Media Informatika Edisi September - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i1.6083

Abstract

Penelitian ini bertujuan membandingkan kinerja tiga algoritma klasifikasi—Naïve Bayes, C4.5, dan K-Nearest Neighbor (K-NN)—dalam menentukan kelayakan penerima Program Keluarga Harapan (PKH) di Rantau Prapat. Dataset terdiri dari 109 data keluarga dengan variabel seperti pendapatan, jumlah tanggungan, status pekerjaan, dan kepemilikan aset. Pengolahan dan analisis data dilakukan menggunakan RapidMiner Studio, dengan evaluasi kinerja berdasarkan akurasi, presisi, recall, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa algoritma C4.5 memberikan kinerja terbaik dengan akurasi 91,8%, presisi 90,7%, recall 92,3%, dan AUC 0,944. Naïve Bayes mencatat akurasi 87,2% dan recall 88,9%, sedangkan K-NN menghasilkan akurasi 89,9% dan recall 91,1%, namun memerlukan komputasi lebih tinggi. Temuan ini menunjukkan bahwa C4.5 lebih efektif dalam mengklasifikasikan kelayakan penerima PKH secara akurat dan efisien. Penelitian ini menegaskan potensi algoritma machine learning dalam mendukung pengambilan keputusan pada program bantuan sosial. Studi lanjutan disarankan untuk memperluas cakupan data dan mengeksplorasi metode klasifikasi lainnya guna optimalisasi distribusi bantuan.
Socialization and Implementation of a Midwifery Education Chatbot at the Rantauprapat City Community Health Center Fadillah, Riszki; Ramadani, Putri; Adawiyah, Quratih; Fitriyani, Intan Nur
International Journal of Community Service (IJCS) Vol. 4 No. 1 (2025): January-June
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijcs.v4i1.1080

Abstract

Improving the quality of maternal healthcare requires an innovative, technology-based approach, particularly in providing midwifery education. This community service project aimed to introduce and train pregnant women at the Rantauprapat City Community Health Center (Puskesmas) in the use of an educational chatbot based on the Recurrent Neural Network (RNN) algorithm. This chatbot was designed to provide fast, relevant, and accessible pregnancy health information. The activity involved coordination with partner health centers, outreach, hands-on training on the use of the chatbot, and evaluation of its effectiveness. The evaluation results showed that more than 90% of participants felt the chatbot helped them understand their pregnancy status, with the majority of questions related to early symptoms, diet, and safe activities during pregnancy. Furthermore, health workers stated that the chatbot could ease the burden of answering repetitive questions from patients. The implementation of this technology has significantly contributed to improving digital-based midwifery literacy and strengthening the role of community health centers as primary health care centers that are adaptive to technological developments. Going forward, the development of additional features and the expansion of local content are expected to strengthen the use of the chatbot on a broader scale.
Co-Authors Adawiyah, Quratih Al Ghazali, Baginda Restu Asy’ari, Ilham Atika Sadariah Nasution Baginda Restu Al Ghazali Chairul Huda D, Novica Jolyarni Desi Irpan Dinda Salsabila Ritonga Fahruzi Sirait Intan nur Fitriyani Intan Nur Fitriyani INTAN NUR FITRIYANI JOLYARNI D, NOVICA Mardivta, Hafizhah Mesran, Mesran Muhammad Amin Nabila Keysa Erianti Nur Indah Nasution Purwanto, Juni Putri Ramadani Rahadatul 'Aisy Riadi Ramadani, Putri Ramadani, Ramadani Rani Darma Sakti Tanjung RINA ANGGRAINI Riswan Syahputra Damanik Riyan Agus Faisal Rizalina Rizalina Sentosa Pohan Sirait, Fahruzi Suerni, Suerni Supriyanto, Boby Tamaza, Muhammad Abyanda Tanjung, Rani Darma Sakti Tusakdiyah Harahap, Halimah Tusakdiyah, Halimah Wahyu, Riski Ferita Yusril Iza Mahendra Hasibuan