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All Journal Jurnal Sains dan Seni ITS Teknika: Jurnal Sains dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia IJISTECH (International Journal Of Information System & Technology) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Varian IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Fundamental Sciences eProceedings of Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Fa'rifah, Riska Yanu
Universitas Telkom
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Analisis Survival Faktor-Faktor yang Mempengaruhi Laju Kesembuhan Pasien Penderita Demam Berdarah Dengue (DBD) di RSU Haji Surabaya dengan Regresi Cox Riska Y. Fa’rifah; Purhadi Purhadi
Jurnal Sains dan Seni ITS Vol 1, No 1 (2012): Jurnal Sains dan Seni ITS (ISSN 2301-928X)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (247.359 KB) | DOI: 10.12962/j23373520.v1i1.2061

Abstract

Tahun 2010, DBD di Indonesia merupakan suatu penyakit yang tergolong pada kejadian luar biasa (KLB) dengan jumlah kejadian sebanyak 156.086 kasus dan kematian sebanyak 1.358. Dari jumlah tersebut, sebanyak 26.059 kasus dan 233 kematian terjadi di Jawa Timur. Untuk mengurangi angka kematian akibat DBD, maka penelitian ini akan memodelkan  waktu survival pasien penderita DBD yang dirawat di RSU Haji Surabaya dengan faktor-faktor yang diduga mempengaruhi laju kesembuhan pasien menggunakan analisis survival regresi cox dengan distribusi Weibull. Berdasarkan hasil analisis, diperoleh informasi bahwa dari 66 pasien sebesar 67% (44) pasien laki-laki, 50% (33) pasien berusia 0-14 tahun, 70% (46) pasien dengan jumlah trombosit di bawah normal (< 150.000/mm3) serta faktor-faktor yang mempengaruhi laju kesembuhan pasien penderita DBD adalah usia dan trombosit di bawah normal. Ketika antar pasien jumlah trombositnya sama, maka risiko untuk sembuh dari pasien yang berusia satu tahun lebih tua akan lebih lama dari pada yang berusia satu tahun lebih muda dan ketika usia pasien sama, dengan jumlah trombosit di bawah normal akan mencapai sembuh lebih lama daripada pasien dengan jumlah trombosit yang normal.  
Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm Riska Yanu Fa'rifah; Dita Pramesti
Jurnal Varian Vol 5 No 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i2.1894

Abstract

This study aims to cluster 38 Districts/Cities in East Java based on the 10 forming indicators of inclusive economic development and to determine the inclusive economic growth of Districts/Cities above or below the total average. 10 indicators used in this study are GRDP per capita, GRDP by business field, Labor force participation rate, Unemployment rate, Gini ratio, Expenditure per capita, the number of poverty, Life expectancy, expectation years of schooling, and mean years of schooling. There are 3 scenarios in this study, namely 2 clusters, 3 clusters, and 4 clusters. The method of clustering in this study is using the K-means algorithm. This study uses the silhouette coefficient to evaluate the best cluster of 3 scenarios. The best k-means algorithm in this study is using 2 clusters with a silhouette coefficient of 0.87. There are 29 Districts/Cities included in cluster 1 with inclusive economic development below the total average and 9 Districts/Cities included in cluster 2 with inclusive economic development above the total average. The members of cluster 1 are mostly district areas and located in coastal or border areas and the members of cluster 2 are mostly urban or industrial areas.
RANCANG BANGUN APLIKASI E-VOTING PEMILIHAN KETUA UMUM HIMPUNAN MAHASISWA INFORMATIKA (HMTI) UNIVERSITAS COKROAMINOTO PALOPO BERBASIS WEBSITE Nurul Samania; Nirsal; Riska Yanu Fa'rifah
Jurnal Ilmiah Teknologi Informasi Vol 10 No 1 (2020): Edisi Januari 2020
Publisher : Universitas Cokroaminoto Palopo Fakultas Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (110.462 KB)

Abstract

Penelitian ini bertujuan untuk merancang dan membangun aplikasi e-voting yang digunakan untuk pemilihan Ketua Umum HMTI berbasis website sehingga mampu menggantikan sistem voting pemilihan konvensional. Pada penelitian ini ada tiga metode yang dijadikan sebagai cara yang dilakukan oleh penulis untuk menganalisa kebutuhan dan melakukan perencanaan serta mengumpulkan data, yaitu metode wawancara, metode observasi, dan metode pustaka sehingga input maupun output dari aplikasi e-voting yang dibangun dapat seperti dengan yang diharapkan. Adapun software yang digunakan adalah PHP sebagai bahasa pemrograman, MySQL untuk pengolahan basis data dan Xampp sebagai servernya. Teknik pengujian yang digunakan dalam sistem ini yaitu teknik pengujian Black Box Testing, berdasarkan pengujian yang telah dilakukan bahwa Rancang Bangun Aplikasi E-Voting Pemilihan Ketua Umum Himpunan Mahasiswa Informatika (HMTI) Universitas Cokroaminoto Palopo Berbasis Website layak dan sesuai dengan fungsi yang dibutuhkan. Hasil rancang bangun website ini dapat digunakan untuk pemilihan Ketua Umum HMTI dan telah disesuaikan terlebih dahulu sehingga dapat memudahkan pihak dalam mengoperasikan sistem tersebut.
ANALISIS CLUSTER K-MEANS DALAM PENGELOMPOKAN KEMAMPUAN MAHASISWA B. Poerwanto; R.Y. Fa’rifah
Indonesian Journal of Fundamental Sciences Vol 2, No 2 (2016)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.833 KB) | DOI: 10.26858/ijfs.v2i2.2434

Abstract

Abstract. Cluster Analysis, K-Means Algorithm, Student Classification. This study aims to classify students based on learning outcomes for subject the basic of statistics (DDS), which is measured based on attendance, task, midterm (UTS), and final exams (UAS) to further used to evaluate learning for subjects that require analysis of quantitative . This study uses k-means cluster analysis to classify the students into three groups based on learning outcomes. After grouped, there are 3 people in the low category, 27 in the medium category and over 70% in the high category.Abstrak. Analisis Cluster K-Means dalam Pengelompokan Kemampuan Mahasiswa. Pene-litian ini bertujuan untuk mengelompokkan mahasiswa berdasarkan hasil belajar mata kuliah dasar-dasar statistika (DDS) yang diukur berdasarkan variabel nilai kehadiran, tugas, ujian tengah semester (UTS), dan ujian akhir semester (UAS) untuk selanjutnya digunakan untuk mengevaluasi pembelajaran untuk mata kuliah yang membutuhkan kemampuan analisis kuantititatif yang baik. Penelitian ini menggunakan analisis cluster k-means dalam mengelompokkan mahasiswa ke dalam tiga kelompok berdasarkan hasil belajarnya. Seteleh dikelompokkan, terdapat 3 orang yang masuk pada kategori rendah, 27 orang pada kategori sedang dan lebih dari 70% pada kategori tinggi.Kata Kunci: Cluster Analysis, K-Means Algoritma, Klasifikasi Mahasiswa, Universitas Cokroaminoto Palopo
Klasifikasi Laju Pernapasan dan Saturasi Oksigen Menggunakan Regresi Logistik Alfi Zahra Hafizhah; Sinung Suakanto; Riska Yanu Fa&#039;rifah; Edi Triono Nuryatno
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 2 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i2.3481

Abstract

Saturasi oksigen dan laju pernapasan adalah dua parameter dasar yang digunakan untuk menilai kondisi pasien, khususnya pernapasan. Gagal jantung dan COVID-19 adalah beberapa penyakit yang berhubungan dengan dua parameter ini. Gagal jantung memiliki gejala pernapasan spesifik seperti nyeri dada dan sesak napas, yang disebabkan oleh ketidaknormalan pada saturasi oksigen dan laju pernapasan. COVID-19 merupakan penyakit yang baru ditemukan pada tahun 2019 dan penyakit ini juga memiliki keterkaitan yang dekat dengan pernapasan. Jika terinfeksi, COVID-19 dapat menyebabkan acute respiratory distress syndrome (ARDS), pneumonia, dan permasalahan dengan organ tubuh lainnya, yang dapat menyebabkan kematian bagi penderitanya. Maka dari itu, kedua parameter ini sangat penting untuk menentukan kondisi pernapasan pasien. Penelitian ini bertujuan untuk membangun sebuah model regresi logistik untuk mengklasifikasikan kondisi pernapasan pasien menggunakan saturasi oksigen dan laju pernapasan sebagai parameter. Regresi logistik digunakan karena kecocokan dari kelebihan model dengan data yang digunakan dalam penelitian dan algoritma ini dapat menjelaskan pengaruh parameter-parameter independen yang digunakan terhadap parameter dependennya. Kemudian model ini akan di evaluasi menggunakan metode F1-Macro. Penyelesaian penelitian menggunakan CRISP-DM metodologi, serta mempersiapkan data menggunakan metode downsampling dan mengategorikan nilai dari variabel-variabel untuk mendapatkan hasil model yang lebih baik. Akurasi dari model testing adalah 87.5%, sementara akurasi evaluasi menggunakan F1-Macro adalah 87%. Hasil dari penelitian ini juga sudah sesuai dengan teori medis yang dilihat dari interpretasi koefisien saturasi oksigen dan laju pernapasan.
Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm Putri Kurniawati; Riska Yanu Fa'rifah; Deden Witarsyah
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4265

Abstract

The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.
PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE Sheva Aditya Helmayanti; Faqih Hamami; Riska Yanu Fa’rifah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.415

Abstract

The development of the internet has changed people's lifestyle with the existence of FinTech. One of the popular FinTech innovations is the Flip digital wallet application. In this study, aspect-based sentiment analysis was carried out on Flip user reviews using the naive bayes algorithm. The test results show high accuracy, with an average accuracy of 0.84. The naive bayes algorithm is effective in classifying user reviews based on aspects of speed, security, and cost, with accuracies of 0.80, 0.87, and 0.84, respectively. This research provides important insights for service providers to improve service performance and innovation. The labelling data generated the most sentiment 0 (no sentiment), followed by sentiment 1 (positive) and 2 (negative). Negative sentiments have a high frequency on speed and security aspects, while positive sentiments have a high frequency on cost aspects. Thus, improvements are needed to the security system and speed of the Flip application to increase user satisfaction in these aspects. The naive bayes algorithm can be a useful tool in processing review data on e-wallet applications and similar services.
ASPECT-BASED SENTIMENT ANALYSIS TERHADAP ULASAN APLIKASI FLIP MENGGUNAKAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBORS (K-NN) Ferda Ayu Dwi Putri Febrianti; Faqih Hamami; Riska Yanu Fa’rifah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.429

Abstract

The rapid growth of online transactions in Indonesia has increased the demand for efficient interbank transfer solutions. However, the costs associated with such transactions have become a significant obstacle. Flip, a company with a vision to become a global leader in customer satisfaction-driven services, offers a solution to this challenge. This study proposes an aspect-based sentiment analysis method using the K-Nearest Neighbors (K-NN) algorithm to analyze user sentiment on key aspects, namely speed, security, and the cost of using the Flip application. The results of this research provide valuable information that can be used as a basis to provide insights, suggestions and recommendations to businesses, so they can create better solutions and promote optimal user experience. The research results show that the K-NN model has the ability to predict user psychology well in all aspects, with a significant level of accuracy, specifically speed (73.04%), security (86, 05%) and costs (80.11%). In addition, this study also compares two model validation methods: simple data splitting method and K-Fold cross-validation. Although the simple data splitting method has a higher average accuracy, K-fold cross-validation is considered superior as it provides a more accurate and reliable estimate of the overall performance of the model. Sentiment analysis results show that Flip app users tend to give negative feedback on speed and security, while they give positive feedback on cost. Therefore, the main recommendation is that the company PT Fliptech Lentera Inspirasi Pertiwi improves the speed and security aspects to increase user satisfaction with the Flip application. Therefore, this customer-centric service will continue to prioritize user satisfaction as its primary goal.
Aspect-Based Sentiment Analysis On FLIP Application Reviews (Play Store) Using Support Vector Machine (SVM) Algorithm Nurul Hidayati; Faqih Hamami; Riska Yanu Fa’rifah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9768

Abstract

The development of fintech has driven the rapid growth of e-wallets like Flip, offering a convenient solution for interbank transfers without administrative fees. User reviews on the Play Store serve as crucial feedback for understanding the user experience. This research utilizes aspect-based sentiment analysis (ABSA) in combination with the SVM method to detect opinions, perceptions, and reviews pertaining to Flip's speed, security, and cost aspects. The objective is to provide valuable insights to both users and companies regarding their experiences with Flip in conducting financial transactions. The study employs a dataset comprising 13,500 preprocessed and cleansed data points, followed by TF-IDF vectorization. The data is divided into training and testing sets, utilizing techniques such as the train-test split and K-Fold Cross Validation to assess model performance. GridSearch analysis reveals that specific parameter combinations, notably C=1.0 and test_size=0.1, yield high accuracy across all aspects, with the linear kernel displaying the highest overall accuracy. Model evaluation is conducted using the confusion matrix and classification report, presenting accuracy, precision, recall, and F1-scores for each aspect. Notably, the Support Vector Machine model performs well, particularly in the speed, security, and cost aspects, where the cost aspect demonstrates exceptionally strong results. In summary, this study employs ABSA to analyze Flip application reviews, with the Support Vector Machine model showcasing impressive performance across various aspects, providing valuable insights for users and companies engaging with Flip's financial transaction services.Keywords: aspect-based sentiment analysis, support vector machine, reviews, Flip
Analisis Sentimen Trend Makanan Dan Minuman dengan Support Vector Machine Sebagai Rekomendasi Peluang Bisnis Bagi UMKM Ahmad Fauzi; Riska Yanu Fa’rifah; Ekky Novriza Alam
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.249

Abstract

The trend regarding food and beverages in recent years has been widely discussed and has significantly impacted the activities of Small and Medium-sized Enterprises (SMEs) that sell food and beverages. Therefore, SMEs engaged in food and beverage sales should pay attention to public opinions regarding food and beverage trends, as well as observe the patterns of emerging food and beverage trends that are currently trending. To comprehend public sentiments concerning food and beverage trends, this study implements the Support Vector Machine (SVM) algorithm to assess its capacity in analyzing positive and negative sentiments within comments related to food and beverage trends on Twitter. The dataset employed consists of tweets created from the year 2018 to 2021, related to food and beverage trends. This dataset underwent several stages of processing, including preprocessing, data division into training and testing sets, and the application of weights using the TF-IDF method. Subsequently, the data is processed using the SVM algorithm. This study employs three ratios for data division: 90:10, 80:20, and 70:30 for training and testing. The most significant accuracy results for each dataset are as follows: 91.19% for "Es Kepal Milo" with a 90:10 ratio, 91.78% for "Baso Aci" with a 90:10 ratio, 87.98% for "Dalgona" with a 90:10 ratio, and 92.34% for "Corndog" with a 90:10 ratio. The implementation of the SVM algorithm yields high accuracy values, indicating that SVM is suitable for sentiment analysis of food and beverage trends. To understand the patterns of food and beverage trends, it can be inferred from the analysis conducted on food trends from 2018 to 2021 that the rise in food and beverage trends in Indonesia lasts for only one (1) to three (3) months before experiencing a sustained and drastic decline. Hence, SME stakeholders engaged in food and beverage sales need to prepare for such conditions.
Co-Authors Afdillah, Dwivi Ahmad Fauzi Alfi Zahra Hafizhah Ariandi, Rama Arrahmani, Farras Hilmy Azzahra, Zalina Fatima B. Poerwanto Deden Witarsyah Deden Witarsyah Dita Pramesti Edi Triono Nuryatno Ekky Novriza Alam Faqih Hamami Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Helmayanti, Sheva Aditya Hutasoit, Michael Nico I Gede Pasek Suta Wijaya Jauhari, M.Habib Kadriyan, Hamsu Kuswandi, Brillian Adhiyaksa Mufriz, Muhammad Fadwa Muhaimin, Luthfi Ahmad Mulyana, Heru Nirsal Nisa, Amarila Zahratun Nur Ichsan Utama Nurul Hidayati Nurul Samania Oktariani Nurul Pratiwi Pariswara, Endar Poedjimartojo, Naufal Avilandi Pramudia, Rudi Guna Pramuwidya, Shinta Purhadi Purhadi Putri Kurniawati Rachmadita Andreswari Rahmah, Najma Syarifa Sheva Aditya Helmayanti Sinung Suakanto Siregar , Anggiat Pandu Daniel Syfa Nur Lathifah Witarsyah , Deden