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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Sisfo: Jurnal Ilmiah Sistem Informasi JIKA Indonesian Journal of Innovation Multidisipliner Research Journal Of Artificial Intelligence And Software Engineering Solusi Bersama : Jurnal Pengabdian Dan Kesejahteraan Masyarakat Indonesian Journal of Innovation Multidisipliner Research
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Implementasi Algoritma K-Means dalam Menentukan Clustering pada Penilaian Kepuasan Pelanggan di Badan Pelatihan Kesehatan Pekanbaru Aqshol Al Fahrozi; Fitri Insani; Elvia Budianita; Iis Afrianty
Indonesian Journal of Innovation Multidisipliner Research Vol. 1 No. 4 (2023): Oktober - Desember
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijim.v1i4.53

Abstract

This research discusses the implementation of the K-Means algorithm in determining clustering in customer satisfaction assessments at the Pekanbaru Health Training Agency. Customer satisfaction is the level of a person's feelings to perceive the comparison between the consumer's impression of the level of product and service performance and the customer's or buyer's expectations. The aim of this research is to see the level of customer satisfaction with the Pekanbaru Health Training Agency (Bapalkes) services using K-means clustering and how high the level of customer satisfaction is using the K-means Clustering method. In this research, the data used is Health Training Center customer data from 2019 and 2023. Data was collected through questionnaires distributed via Google form. Creating a rule model for the collected data using the k-means algorithm and rapidminer software. From the research results obtained using the K-Means algorithm in clustering customer data, it can provide customer segmentation results that are in line with expectations, so that the Pekanbaru Health Training Agency can easily understand the characteristics of its customers based on their clusters and their satisfaction. Then, using the elbow and Davies Bouldin methods, we also provide a solution for selecting the right number of clusters so that performance is more optimal and produces more accurate customer segmentation results. From the calculations of the k-means algorithm, it was obtained that the response value was very dominant at 259 who expressed satisfaction and 44 people who expressed dissatisfaction from 303 customers, so that the k-means algorithm used sensitivity and specificity tests, 86% expressed satisfaction and 14% expressed dissatisfaction with services provided by the Pekanbaru Health Training Agency.
Implementasi Algoritma K-Means dalam Menentukan Clustering pada Penilaian Kepuasan Pelanggan di Badan Pelatihan Kesehatan Pekanbaru Aqshol Al Fahrozi; Fitri Insani; Elvia Budianita; Iis Afrianty
Indonesian Journal of Innovation Multidisipliner Research Vol. 1 No. 4 (2023): Oktober - Desember
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijim.v1i4.53

Abstract

This research discusses the implementation of the K-Means algorithm in determining clustering in customer satisfaction assessments at the Pekanbaru Health Training Agency. Customer satisfaction is the level of a person's feelings to perceive the comparison between the consumer's impression of the level of product and service performance and the customer's or buyer's expectations. The aim of this research is to see the level of customer satisfaction with the Pekanbaru Health Training Agency (Bapalkes) services using K-means clustering and how high the level of customer satisfaction is using the K-means Clustering method. In this research, the data used is Health Training Center customer data from 2019 and 2023. Data was collected through questionnaires distributed via Google form. Creating a rule model for the collected data using the k-means algorithm and rapidminer software. From the research results obtained using the K-Means algorithm in clustering customer data, it can provide customer segmentation results that are in line with expectations, so that the Pekanbaru Health Training Agency can easily understand the characteristics of its customers based on their clusters and their satisfaction. Then, using the elbow and Davies Bouldin methods, we also provide a solution for selecting the right number of clusters so that performance is more optimal and produces more accurate customer segmentation results. From the calculations of the k-means algorithm, it was obtained that the response value was very dominant at 259 who expressed satisfaction and 44 people who expressed dissatisfaction from 303 customers, so that the k-means algorithm used sensitivity and specificity tests, 86% expressed satisfaction and 14% expressed dissatisfaction with services provided by the Pekanbaru Health Training Agency.
Application of ADASYN and Bayesian Optimization to Random Forests for Cervical Cancer Classification Restu Kharrisa Andini; Iis Afrianty; Muhammad Fikry; Fadhilah Syafria
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26973

Abstract

Accurate early detection is crucial for reducing mortality rates from cervical cancer. However, the application of machine learning to medical data is often hindered by class imbalance, causing prediction results to be biased toward the majority class. On the other hand, the process of parameter search using conventional methods such as GridSearchCV requires significant computational time. Therefore, this study proposes the application of the ADASYN (Adaptive Synthetic Sampling) method and Bayesian optimization to the Random Forest algorithm. In its implementation, ADASYN is used to adaptively synthesize minority data samples to rebalance their distribution. Meanwhile, Bayesian optimization serves to determine the optimal hyperparameter combination through a faster probabilistic approach. Model evaluation was conducted across four testing scenarios with training-to-test data splits of 90:10, 80:20, and 70:30. Findings from this study indicate that the standard Random Forest algorithm still produces biased predictions. However, classification performance improved significantly after the model was combined with ADASYN and Bayesian Optimization. The optimal results were achieved at a 70:30 ratio, recording accuracy of 98.06%, precision of 97.03%, recall of 99.13%, and an F1-score of 98.07%, with a computation time of 32.66 seconds. Overall, the proposed model successfully addresses data imbalance while reducing optimization time, enabling it to predict biopsy diagnoses with high precision.
Penerapan Algoritma Fuzzy C-Means untuk Pengelompokan Kepuasan Masyarakat terhadap Layanan Berdasarkan Dimensi SERVQUAL Ramadhani Herfin; Fadhilah Syafria; Elvia Budianita; Iis Afrianty; Salmiyati Salmiyati
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10063

Abstract

Pekanbaru Public Service Mall (MPP) is an integrated service facility that brings together various government agencies in one location. The problem identified is the absence of an in-depth mapping of community satisfaction levels that can realistically represent satisfaction gradations, as the previous approach using K-Means Clustering is crisp in nature and unable to represent the subjective satisfaction of humans who may belong to more than one category simultaneously. Therefore, this study aims to cluster community satisfaction levels toward MPP Pekanbaru services based on five SERVQUAL dimensions using Fuzzy C-Means, and to identify service dimensions that require priority improvement. Unlike K-Means, Fuzzy C-Means allows each respondent to hold membership degrees in multiple clusters simultaneously, making it more suitable for multidimensional satisfaction data. Data were collected through questionnaires distributed to 532 respondents with 23 Likert-scale items (1–5) in accordance with five SERVQUAL dimensions and PermenPANRB Number 14 of 2017. The optimal number of clusters was determined using the Partition Coefficient Index (PCI) by testing four scenarios (c=2, 3, 4, 5). PCI evaluation results showed that c=2 is the optimal configuration with the highest PCI value of 0.799303, achieving convergence at the 12th iteration. Clustering results revealed that 283 respondents (53.2%) belong to Cluster 1 labeled Very Satisfied and 249 respondents (46.8%) belong to Cluster 2 labeled Satisfied. Per-dimension SERVQUAL analysis identified Responsiveness as the primary improvement priority with the largest inter-cluster gap (1.1857 points). The contribution of this research is to produce a Fuzzy C-Means-based community satisfaction clustering model capable of representing satisfaction gradations more realistically than crisp approaches, and to provide a SERVQUAL-based service improvement priority map that can serve as an evaluation reference for MPP Pekanbaru management and other public service institutions.
Analisis Komparatif Jarak Euclidean, Manhattan, Canberra, Chebyshev, Cosine pada K-Means untuk Evaluasi Kepuasan Masyarakat Fakhri Fakhri; Iis afrianty; Elvia Budianita; Fadhilah Syafria; Siska Kurnia Gusti; Salmiyati Salmiyati
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10086

Abstract

The selection of distance metrics in the K-Means Clustering algorithm can affect the quality of clustering results, particularly on public satisfaction data measured using a Likert scale. This study aims to compare the performance of five distance metrics, namely Euclidean Distance, Manhattan Distance, Canberra Distance, Chebyshev Distance, and Cosine Similarity, in clustering the level of public satisfaction toward public services. The research data were obtained from 533 respondents who used the services of the Mal Pelayanan Publik (MPP) Pekanbaru through a questionnaire consisting of 23 questions based on the SERVQUAL dimensions and the Community Satisfaction Survey indicators in accordance with PERMEN PAN-RB Number 14 of 2017. After the data cleaning process, one duplicate record was removed, resulting in 532 respondent records used in the analysis stage. The number of clusters was determined using the Elbow Method, while cluster quality was evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results show that Manhattan Distance with k=2 produced the lowest DBI value of 0.8144, whereas Euclidean Distance with k=3 produced the highest Silhouette Score of 0.5088. The clustering results formed groups of respondents with different satisfaction levels, namely Dissatisfied, Satisfied, and Very Satisfied. This study contributes an evaluative comparison of five distance metrics in the K-Means algorithm using two evaluation approaches simultaneously, namely the Davies-Bouldin Index and Silhouette Score, on public satisfaction data based on a Likert scale. The results indicate that the performance of distance metrics may differ depending on the evaluation method used, therefore the selection of distance metrics should consider the characteristics of the data and the objectives of the analysis.The difference in evaluation results indicates that DBI and Silhouette Score assess clustering quality from different aspects. Based on the findings, Manhattan Distance and Euclidean Distance demonstrated better performance compared to other distance metrics on the dataset used, and can therefore be considered in the analysis of public satisfaction toward public services.
Information Gain and Random Forest for Sex Classification Based on Craniometric Measurements Nabilla Alya Firana; Iis Afrianty; Novriyanto Novriyanto; Febi Yanto
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9409

Abstract

Sex identification from human skulls is a crucial aspect of forensic anthropology; however, traditional methods still face limitations such as subjective assessment and inter-population variation. This study proposes the application of Information Gain as a feature selection technique and Random Forest as a classification algorithm for sex determination based on craniometric data. The dataset used is the Howells dataset consisting of 2,524 samples with 83 skull measurement features. Feature selection using Information Gain was performed with threshold values of 0.01, 0.05, and 0.09, followed by additional testing across a threshold range of 0.01 to 0.09. Model evaluation was conducted using 10-Fold Cross Validation with default Random Forest parameters. The results show that a threshold of 0.02 produced 57 selected features from the original 83, achieving the best performance with an accuracy of 87.40%, precision of 87.53%, recall of 87.40%, and F1-score of 87.41%. These results outperform the baseline model without feature selection, which achieved an accuracy of 86.57%. This study demonstrates that Information Gain feature selection can reduce data dimensionality by 31.3% while simultaneously improving sex classification performance based on craniometric data.
Application of Information Gain Feature Selection and SMOTE in XGBoost Algorithm for Asthma Disease Classification Fioni Nikmatul Fajar; Fitri Insani; Suwanto Sanjaya; Iis Afrianty
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9384

Abstract

Asma merupakan salah satu penyakit kronis pada sistem pernapasan yang prevalensinya terus meningkat dan memerlukan deteksi dini untuk mencegah komplikasi serius. Salah satu tantangan dalam klasifikasi asma menggunakan machine learning adalah ketidakseimbangan kelas yang menyebabkan model cenderung memprediksi kelas mayoritas sehingga kemampuan mendeteksi kasus asma menjadi rendah. Penelitian ini mengusulkan penerapan SMOTE dan seleksi fitur Information Gain dalam algoritma XGBoost untuk mengatasi permasalahan tersebut. Dataset yang digunakan terdiri dari 2.392 data dengan 28 atribut, di mana tahapan penelitian meliputi preprocessing, seleksi fitur menggunakan Information Gain yang mengurangi fitur menjadi 22 fitur, penyeimbangan data menggunakan SMOTE, pembagian data dengan rasio 90:10, 80:20, dan 70:30, serta klasifikasi menggunakan XGBoost. Pengujian dilakukan terhadap empat skenario pendekatan untuk membandingkan kontribusi setiap metode yang diterapkan. Evaluasi dilakukan menggunakan data uji seimbang dan data uji asli dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa skenario terbaik diperoleh pada kombinasi Information Gain + SMOTE + XGBoost dengan rasio 90:10 pada data uji seimbang, menghasilkan akurasi 75%, presisi 87,5%, recall 58,33%, dan F1-score 70%. Hasil tersebut menunjukkan bahwa kombinasi seleksi fitur dan penyeimbangan data mampu meningkatkan kemampuan model dalam mendeteksi penyakit asma.
GAMBARAN KARAKTERISTIK IBU POST SECTIO CESAREA TERKAIT PENYEMBUHAN LUKA Ekawati Saputri, Saputri; Iis Afrianty; Evodius Nasus
Jurnal Ilmu Kesehatan Abdurrab Vol. 1 No. 4 (2023): Vol 1 No 4 Desember 2023
Publisher : LPPM Universitas Abdurrab

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

Abstract

Sectio caesarea is a delivery method that is carried out by making an open incision in the uterine wall, causing wounds in the abdominal area. Globally, around 21% of caesarean section deliveries occur. In Indonesia, caesarean section delivery is around 17.6%. This study aims to determine the characteristics of post-cesarean section mothers regarding wound healing. This research is descriptive quantitative research with a Secondary Data Analysis (ADS) approach. The total sample was 136 using purposive sampling technique. The results of this study show that the characteristics of mothers post cesarean section are that most of them are aged 20-35 years (76.5%) with secondary education level (46.3%), multiparous (69.1%), and have no history of CS (56, 6%) and did not suffer from anemia (61.8%). Almost all of the wounds experienced by mothers after caesarean section were dry wounds (99.3%). The post caesarean section wound is healing well.
Co-Authors Agung Surya Maulana Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Aqshol Al Fahrozi Arif Mudi Priyatno Boni Iqbal Darmila Dewi Nasien Dinyah Fithara efni humairah Eka Pandu Cynthia Ekawati Saputri Ekawati Saputri, Saputri Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvia Budianita Erni Rouza, Erni Evodius Nasus Fadhilah Syafria Faizah Husniah Fakhri Fakhri Febi Yanto Felian Nabila Fioni Nikmatul Fajar Fitra Lestari Fitri Insani Fitri Insani Fratiwi Rahayu grace tedy tulak Gusti, Siska Kurnia Habibollah Haron Ihlal Hanafi Harahap Ikhsanul Hamdi Ismail marzuki Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Karina Julita Lestari Handayani Mawadda Warohma Mentari Aulia Putri Muhammad Fikry Muhammad Fikry Muhammad Habib Nazlis Muhammad Irsyad Nabilla Alya Firana Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Nurafni Syahfitri Nurul Fatiara Purnama, Nilam Puspa Melani Almahmuda Qurrata A'yuni Rahayu Anggraini Rahmad Abdillah Rahmad Kurniawan Rahmat Al Hafiz Ramadhani Herfin Ravi Umbara Reski Mai Candra Restu Kharrisa Andini Riyan Wibowo Saputra Robby Azhar Sahrul Poalahi Salu Said Nurfan Hidayad Tillah Salmiyati Salmiyati Sephia Pratista SURYA ADITYA GD Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaiful Azhar Ummy Agustina Putri Yusra Yusra