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All Journal Journal of Innovation and Future Technology (IFTECH) IJISTECH Jurnal Media Informatika International Journal of Community Service (IJCS) Jurnal Manajemen Informatika dan Bisnis Digital International Journal of Health, Engineering and Technology Journal of Innovative and Creativity Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Sevaka: Hasil Kegiatan Layanan Masyarakat
Putri Ramadani
Institut Teknologi dan Kesehatan Ika Bina
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Penerapan Metode Naïve Bayes Dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen Putri Ramadani; Gunadi Widi Nurcahyo; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.361

Abstract

Student satisfaction in higher education is the main focus in improving the quality of education. In the Tridharma paradigm, satisfaction is measured through a comparison of expectations and teaching realization as the main indicator of learning effectiveness. This research method uses Naïve Bayes classification, through the steps of reading training data, calculating prior probabilities, training data probabilities for each category, reading testing data, and calculating final probabilities. This research aims to evaluate student satisfaction with lecturers' teaching at the LP3I Polytechnic, Padang Campus. The data used in this research was 574. The results of research with 574 data (516 training and 58 testing) showed that 52 data (89.648%) stated "Very Satisfied", while 6 data (10.344%) stated "Satisfied". Prediction accuracy reached 98.28%. However, when using the Naïve Bayes method with 574 data (574 training and 574 testing), 397 data (69.078%) stated "Very Satisfied" and 177 data (30.798%) stated "Satisfied". Without the Naïve Bayes method, 402 data (69.948%) stated "Very Satisfied" and 172 data (29.928%) stated "Satisfied". An improvement of 0.87% occurred for the "Very Satisfied" category and -0.87% for "Satisfied". There are no differences in percentages for other categories. From the comparison of results, it can be seen that the Naïve Bayes method is superior in predicting student satisfaction levels compared to calculations without this method. Therefore, it can be concluded that the Naïve Bayes process model is suitable for use as a method for determining good decisions in predictions
Expert System for Detecting Diseases in Cattle Using Backward Chaining Method Ramadani, Putri; Wahyuni, Alvi Dwi; Putra, Eka Ramadhani
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.383

Abstract

Cow is one of the livestock animals with commercial or economic value due to the sale of beef and bull semen. Livestock diseases can reduce the quality of livestock and cause a decline in sales. This research aims to help farmers recognize or identify types of diseases in cows based on visible symptoms or to prevent the risk of disease to avoid outbreaks. All data used comes from experts and a collection of documents from magazines and books related to livestock diseases. This analysis applies backward chaining in expert systems, particularly systems that process existing facts to reach conclusions. Facts are derived from physical conditions, also called symptoms. Backward chaining is a goal-based analysis that starts with an assumption of what might happen, then searches for facts (evidence) or symptoms that support (or refute) the hypothesis. The development of a web-based expert system makes it easier for farmers to access the system online. The accuracy of the expert system has been tested by stakeholders or experts, resulting in fast, accurate, and effective information. This research can assist farmers in diagnosing symptoms in livestock, and the test results can accurately detect the type of disease in livestock so that treatment can be carried out quickly.
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.
Prediksi hasil belajar mahasiswa pada PBL menggunakan algoritma Decision Tree untuk evaluasi pembelajaran Irfan, Desi; Ramadani, Putri; Nasution, Atika Sdaariah; Irwansyah, Irwansyah; Ramadan, Joeanda Bagus
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.v6i1.6085

Abstract

Penelitian ini bertujuan untuk mengembangkan model prediksi hasil belajar mahasiswa yang mengikuti pembelajaran berbasis masalah (Problem-Based Learning/PBL) menggunakan algoritma Decision Tree. Model ini dikembangkan dengan memanfaatkan data yang mencakup variabel-variabel seperti Nilai Tugas, Nilai Ujian, Tingkat Partisipasi, dan Waktu Belajar. Data yang digunakan dalam penelitian ini terdiri dari 150 data mahasiswa yang dibagi menjadi 80% untuk data pelatihan dan 20% untuk data uji. Hasil pelatihan model menunjukkan akurasi pelatihan rata-rata sebesar 91%, yang mengindikasikan kemampuan model dalam mempelajari pola-pola dalam data dengan sangat baik. Sementara itu, akurasi pengujian rata-rata sebesar 85.1% menunjukkan bahwa model dapat memprediksi hasil belajar mahasiswa pada data yang belum terlihat sebelumnya dengan ketepatan yang memadai. Meskipun terdapat sedikit perbedaan antara akurasi pelatihan dan pengujian yang mengindikasikan adanya overfitting, model ini tetap efektif dalam mengklasifikasikan hasil belajar mahasiswa. Penelitian ini juga menyarankan penggunaan teknik hyperparameter tuning dan regularisasi untuk meningkatkan performa model, khususnya dalam mengurangi overfitting dan meningkatkan akurasi pada data uji. Secara keseluruhan, hasil penelitian ini menunjukkan bahwa model Decision Tree dapat menjadi alat yang efektif dalam memprediksi hasil belajar mahasiswa dan dapat digunakan untuk memperbaiki strategi pembelajaran berbasis PBL di masa depan.
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.
Penyuluhan Klasifikasi Gejala Keterlambatan Bicara (Speech Delay) Pada Anak Menggunakan Algoritma Naive Bayes, C4.5, Dan K-Nerest Neighbor (K-NN) Putri Ramadani; Ika Ima Nissa; Nur Indah Nasution; Baginda Restu Al Ghazali
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 2 (2024): Mei : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i2.534

Abstract

Speech delay in children is a developmental issue commonly encountered in society, which can affect various aspects of a child's life, including communication, social interaction, and academic development. Early detection of speech delay is crucial for providing appropriate interventions to minimize its long-term impact on the child. This study aims to introduce the use of machine learning algorithms in detecting speech delay symptoms in children. Three machine learning algorithms applied in this study are Naïve Bayes, C4.5, and K-Nearest Neighbor (K-NN). These algorithms are used to classify speech delay symptoms based on health data, medical history, and environmental factors such as speaking habits and eating patterns. The outreach was conducted at Puskesmas Kota Rantauprapat with the involvement of parents and healthcare providers as participants. The experimental results showed that all three algorithms performed well in terms of accuracy, though with varying error rates. Naïve Bayes achieved relatively high accuracy but had a higher false positive rate compared to C4.5 and K-NN. C4.5 provided more stable results and was easier to interpret due to its decision tree structure. Meanwhile, K-NN performed better with data that had irregular distribution. This outreach is expected to assist both the community and healthcare providers in early detection of speech delay in children, providing a more efficient and affordable means for early intervention, which ultimately leads to better outcomes for children with speech delay.
Perkiraan Pola Permintaan Paspor di Kantor Imigrasi dengan Menggunakan Metode Exponential Smoothing untuk Memaksimalkan Layanan Riszki Fadillah; Fitriyani, Intan Nur; Ramadani, Putri; Mardivta, Hafizhah
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 4 No. 2 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v4i2.6789

Abstract

This study aims to analyze the passport application patterns at the Immigration Office and forecast the number of applications for the coming years using the Exponential Smoothing (Holt-Winters) model. The data used includes the number of passport applications from 2022 to 2024. The analysis shows a significant increase in applications in the coming years, with predictions for 2025, 2026, and 2027 indicating a consistent growth pattern. While the model demonstrates good accuracy, the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) calculations indicate overestimation for the 2024 forecast. The application of the Holt-Winters model in forecasting passport applications in the Immigration field is a novel contribution to the literature, as this method is rarely used in this context. The model provides a systematic quantitative approach to predict long-term trends in application data, which is crucial for more efficient service capacity planning. The implications of these findings suggest that, although the model can predict a consistent growth pattern, the overestimation in 2024 highlights the need for model adjustment in the future. Therefore, increasing service capacity through additional staff and optimizing the digital queuing system are strategic steps that should be implemented to handle the projected surge in applications. These measures are essential to ensure efficient service and the Immigration Office's preparedness for the ongoing rise in applications.
Gaya Kepemimpinan Kepala Puskesmas dan Dampaknya terhadap Disiplin Kerja di Puskesmas Kota Rantauprapat Nur Indah Nasution; Nailatun Nadrah; Putri Ramadani; Devi Nur Fitriana
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.2541

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh gaya kepemimpinan kepala Puskesmas terhadap disiplin kerja pegawai di Puskesmas Kota Rantauprapat. Disiplin kerja merupakan salah satu indikator utama dalam mengukur kinerja organisasi, khususnya dalam sektor pelayanan kesehatan yang menuntut ketepatan, ketepatan waktu, serta kepatuhan terhadap standar prosedur operasional (SOP). Tiga gaya kepemimpinan yang dianalisis dalam penelitian ini adalah gaya otoriter, demokratis, dan laissez-faire. Metode penelitian yang digunakan adalah kuantitatif dengan pendekatan deskriptif dan analisis regresi linier sederhana. Data dikumpulkan melalui kuesioner yang dibagikan kepada 60 responden pegawai di berbagai unit kerja di Puskesmas Rantauprapat. Hasil analisis menunjukkan bahwa gaya kepemimpinan demokratis memiliki pengaruh positif yang signifikan terhadap disiplin kerja pegawai (p < 0.05). Sementara itu, gaya otoriter menunjukkan pengaruh yang tidak signifikan, dan gaya laissez-faire memiliki korelasi negatif terhadap disiplin kerja. Interpretasi hasil menyimpulkan bahwa gaya kepemimpinan yang melibatkan pegawai dalam pengambilan keputusan dan memberikan ruang komunikasi dua arah mampu meningkatkan motivasi dan rasa tanggung jawab pegawai, sehingga berdampak positif terhadap kedisiplinan kerja. Implikasi praktis dari temuan ini adalah perlunya pelatihan kepemimpinan bagi kepala Puskesmas untuk mengembangkan pendekatan yang lebih partisipatif dan memberdayakan, guna meningkatkan efektivitas organisasi dan kualitas pelayanan publik.
KESIAPAN ETIKA PENGGUNAAN AI GENERATIF PADA TUGAS AKADEMIK: PENGARUH PEMAHAMAN INTEGRITAS AKADEMIK DAN PERSEPSI MANFAAT-RISIKO Eka Ramadhani Putra; Putri Ramadani; Fitri Safnita
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4526

Abstract

Generative AI tools are increasingly used by students to support academic tasks such as drafting, coding, and summarizing. While these tools may improve efficiency and learning, they also introduce ethical risks related to academic integrity, transparency, privacy, and misinformation. This study examines ethical readiness for using generative AI in academic assignments and tests the effects of students' understanding of academic integrity and their perceived benefit-risk appraisal. A cross-sectional survey was administered to undergraduate students in semester 4 (N = 180). Data were analyzed using multiple regression. Key findings (simulated example): integrity understanding positively predicted ethical readiness (beta = 0.348, p <0.001), perceived risk also showed a positive effect (beta = 0.185, p = 0.013), while perceived benefit was not significant (beta = -0.053, p = 0.498).
Co-Authors Adawiyah, Quratih Ade Irma Yanti Hasibuan Al Ghazali, Baginda Restu Baginda Restu Al Ghazali Billy Hendrik Devi Nur Fitriana Eka Ramadhani Putra Fitri Safnita Fitriyani Nasution Gunadi Widi Nurcahyo Ika Ima Nissa INTAN NUR FITRIYANI Irfan, Desi Irwansyah Irwansyah Mardivta, Hafizhah Nailatun Nadrah Nasution, Atika Sdaariah Nur Indah Nasution Nur Indah Nasution Putra, Eka Ramadhani Ramadan, Joeanda Bagus Riszki Fadillah Riyan Agus Faisal Santosa Pohan Sentosa Pohan Suerni, Suerni Wahyuni, Alvi Dwi Yusril Iza Mahendra Hasibuan