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All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : Computer Engineering and Applications Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Simetris Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Intiqad: Jurnal Agama dan Pendidikan Islam Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fourier InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Proceeding of the Electrical Engineering Computer Science and Informatics JPSE (Journal of Physical Science and Engineering) Jurnal Teknologi dan Sistem Komputer Jurnal Informatika INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK JURNAL MEDIA INFORMATIKA BUDIDARMA BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Telematika Mathvision : Jurnal Matematika Building of Informatics, Technology and Science Transformasi : Jurnal Pendidikan Matematika dan Matematika Jurnal Mnemonic Majalah Ilmiah Matematika dan Statistika (MIMS) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Serambi Engineering
Sari, Dian Candra Rini Novita
Universitas Islam Negeri Sunan Ampel Surabaya
Religion Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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PREDIKSI PRODUKSI BAWANG MERAH DI KABUPATEN NGANJUK DENGAN METODE SEASONAL ARIMA (SARIMA) Noviati Maharani Sunariadi; Putroue Keumala Intan; Dian Candra Rini Novitasari; Yuni Hariningsih
TRANSFORMASI Vol 6 No 1 (2022): TRANSFORMASI : Jurnal Pendidikan Matematika dan Matematika
Publisher : Pendidikan Matematika FMIPA Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/tr.v6i1.1672

Abstract

Produksi bawang merah merupakan komoditas holtikultura yang dikembangkan secara nasional dengan pembinaan yang intensif. Faktor utama yang mempengaruhi produksi bawang merah adalah varietas benih, lahan dan cuaca. Penelitian ini bertujuan untuk memprediksi produksi bawang merah agar komoditas bawang merah dapat menjaga kestabilan harga dan ketersediaan barang di kabupaten Nganjuk. Data pada penelitian ini bersumber dari BPS kabupaten Nganjuk yang digunakan dalam membangun model terbaik dengan metode SARIMA untuk memprediksi produksi bawang merah periode 2021-2023. Berdasarkan hasil analisis yang dilakukan, model terbaik adalah model SARIMA (3,0,2)(2,1,2)12 yang memiliki nilai MAPE sebesar 2,01%.
Analisis Resiko Kanker Serviks Menggunakan PCA-ANFIS Berdasarkan Historical Medical Record Noviati Maharani Sunariadi; Siti Nur Fadilah; Dian Candra Rini Novitasari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.3901

Abstract

Cervical cancer is the second highest disease in the category of women that accounts for the most deaths based on data from WHO. Based on data from the Global Cancer Observatory (Globocan), cervical cancer has a total of 36,633 cases with a death rate of 21,003 cases due to cervical cancer and is relatively high. People with this disease are often difficult to distinguish between healthy and not. So the purpose of this study is to discuss the diagnosis of cervical cancer using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Principal Component Analysis (PCA) reduction to find the best accuracy in the diagnosis of cervical cancer. ANFIS is a system used for modeling based on fuzzy Sugeno, which considers the simplicity of computation. PCA is a method applied to express information expressed in data and specified in an alternative form. The amount of data used is 72 data with 10 features. Then the data is normalized and feature reduction is performed using PCA. After doing feature reduction by PCA obtained 4 influential features. Furthermore, analysis using ANFIS was carried out from the data that PCA extraction was carried out and which was not carried out. Then the accuracy test is carried out using the confusion matrix. The best result based on diagnostic accuracy is ANFIS using 91.67% PCA with the model obtained from the 3rd k-fold and the membership function type is trapmf, while the accuracy without PCA is smaller than using PCA, which is 86.67%.
Implementasi Metode Firefly Algorithm-Extreme Learning Machine (FA-ELM) untuk Peramalan Cuaca Maritim pada Jalur Penyeberangan Ketapang - Gilimanuk Putri Wulandari; Dina Zatusiva Haq; Dian Candra Rini Novitasari
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 2 (2022)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v10i2.49964

Abstract

Cuaca merupakan fenomena yang dinamis. Dalam beberapa tahun terakhir, atmosfer bumi selalu berubah. Keadaan laut berdampak pada kegiatan di pelabuhan, seperti cuaca di laut, angin kencang, pasang surut, dll. Hujan deras menyebabkan kabut menutupi visibilitas kapten, angin kencang, dan ketinggian ombak adalah beberapa persyaratan sebelum keberangkatan transportasi laut. Untuk mengurangi risiko kecelakaan, diperlukan peramalan cuaca maritim dalam beberapa jam ke depan. Penelitian ini, meramalkan parameter cuaca maritim, yaitu, kecepatan angin dan tinggi gelombang di tiga titik untuk jam berikutnya berdasarkan tiga jam sebelumnya menggunakan algoritma Extreme Learning Machine yang telah dioptimalkan bobotnya menggunakan Firefly Algorithm.
Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS Adam Fahmi Khariri; Monika Refiana Nurfadila; Dian Candra Rini Novitasari
Jurnal Informatika Vol 9, No 2 (2022): Oktober 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v9i2.12511

Abstract

Cedera panggul dan acetabular merupakan cedera yang jarang terjadi, terhitung sekitar 3% hingga 8% dari semua cedera. Meskipun angka kematian cedera panggul hanya terbatas pada  1-2%, apabila disertai dengan perdarahan intra-abdominal atau pada intracranial menimbulkan kematian tertinggi yaitu 50%. Kematian akibat cedera panggul terbilang tinggi ketika penanganan awal dan akurat tidak diperhatikan. Pada penelitian ini dilakukan bertujuan mendeteksi tingkat keparahan penderita cedera panggul menggunakan adaptive neuro-fuzzy inference system (ANFIS). Bertujuan membantu medis dalam memberikan penanganan sesuai dengan tingkat keparahan cedera panggul.  Penelitian dengan metode ANFIS untuk mendeteksi keparahan cedera panggul mendapatkan nilai akurasi, presisi, sensitifitas dan F-skor sebesar 100%.
Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine Vina Fitriyana; Lutfi Hakim; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar
Jurnal Buana Informatika Vol. 14 No. 01 (2023): Jurnal Buana Informatika, Volume 14, Nomor 1, April 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i01.6909

Abstract

Sentiment Analysis of Jamsostek Mobile Application Reviews Using the Support Vector Machine Method. Today's technology is evolving quickly, leading to new developments that have helped produce JMO and other mobile applications that can be useful to Indonesians. The reviews or comments in the JMO can be used as a gauge for quality and user satisfaction. This study aims to analyze the quality of JMO applications and classify reviews or opinions into positive, negative, and neutral categories through sentiment analysis. The Support Vector Machine method is used in this analysis process with a linear kernel approach to determine the level of accuracy of classifying JMO application reviews. Research shows that classifying the SVM method against sentiment analysis of reviews or JMO application reviews produces the best accuracy scores, obtaining results with accuracy of 96%, precision of 92%, recall of 96%, and f1-score of 94%, while for the results of most reviews are positive category reviews with a total of 17.571.Keywords: sentiment analysis, JMO, SVM, linear kernel   Perkembangan pesat teknologi saat ini memunculkan inovasi baru untuk menciptakan berbagai aplikasi mobile yang dapat memberi kemudahan bagi masyarakat Indonesia, salah satunya yaitu JMO. Penelitian ini bertujuan untuk menganalisis kualitas aplikasi JMO dan mengklasifikasikan ulasan atau opini kedalam kategori positif, negatif dan netral melalui analisis sentimen. Metode Support Vector Machine digunakan pada proses analisis ini dengan pendekatan kernel linear untuk mengetahui tingkat akurasi dari pengklasifikasian ulasan aplikasi JMO tersebut. Penelitian menunjukkan bahwa pengklasifikasian metode SVM terhadap analisis sentimen ulasan atau review aplikasi JMO menghasilkan nilai akurasi terbaik, didapatkan hasil dengan accuracy 96%, precision 92%, recall 96%, dan f1-score 94%, sedangkan untuk hasil ulasan terbanyak adalah ulasan berkategori positif dengan jumlah 17.571.Kata Kunci: analisis sentimen, JMO, SVM, kernel linear
CLUSTERING DAERAH BANJIR DI JAWA TIMUR DENGAN ALGORITMA FUZZY C-MEANS Ifadah, Corii; Ratnasari, Cristanti Dwi; Novitasari, Dian Candra Rini
Dinamika Informatika : Jurnal Ilmiah Teknologi Informasi Vol 14 No 2 (2022)
Publisher : Fakultas Teknologi Informasi Universitas Stikubank (Unisbank) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.547 KB) | DOI: 10.35315/informatika.v14i2.8885

Abstract

Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review Tasya Auliya Ulul Azmi; Luthfi Hakim; Dian Candra Rini Novitasari; Wika Dianita Utami Dianita Utami
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.8868

Abstract

Purpose: This study aims to monitor the service quality of JMO applications from time to time by classifying JMO user reviews into the class of positive, neutral, and negative sentiments.Design/methodology/approach : The method used in this study is the random forest classification method. Data processing in this study uses feature extraction, TF-IDF and labeling with the lexicon-based method.Findings/result: Based on the research results, it was found that the highest frequency of classification was the positive class with 17571 reviews compared to the neutral class with 8701 reviews and the negative class with 3876 reviews with an accuracy evaluation value of 93%, precision 88%, recall 93%, and f1-score 90%.Originality/value/state of the art:This study uses 150737 reviews that have been pre-processed using the random forest method and TF-IDF and lexicon-based feature extraction.
Classification of Colon Cancer Based on Hispathological Images using Adaptive Neuro Fuzzy Inference System (ANFIS) Nur Hidayah; Alvin Nuralif Ramadanti; Dian Candra Rini Novitasari
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.17611

Abstract

Cancer is a disease that is widely known and suffered by people in various countries. One type of cancer classified as the third contributor to death is colon cancer, with a mortality rate of 9.4%. Colon cancer is cancer that attacks the large intestine or rectum. Classification of colon cancer promptly is necessary to carry out appropriate treatment to reduce the death rate from colon cancer. This study uses the ANFIS method to classify colon cancer with its texture analysis using GLRLM. In addition, the evaluation model used in this study is the K-fold cross-validation method. This research uses colon cancer histopathological image data, which is 10000 image data divided into 2 classes, namely 5000 benign class and 5000 adenocarcinoma class. The best result in this study is when using k = 5 at an orientation angle of 135°, the accuracy value is 85.57%, sensitivity is 91.72%, and specificity is 80.55%.
Pengaruh Reduksi Fitur Pada Klasifikasi Kanker Paru Menggunakan CNN Dengan Arsitektur GoogLeNet Siti Nur Fadilah; Dian Candra Rini Novitasari; Lutfi Hakim
Jurnal Fourier Vol. 12 No. 1 (2023)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2023.121.20-32

Abstract

Kanker paru merupakan jenis kanker dengan penyebab kematian terbanyak. Penelitian ini bertujuan untuk mengklasifikasikan jenis kanker paru apakah termasuk kedalam kelas lung adenocarcinoma, benign lung tissue, lung squamous cell carcinoma berdasarkan citra histopatologi menggunakan metode CNN arsitektur GoogLeNet serta reduksi fitur PCA. Evaluasi model yang digunakan pada penelitian ini menggunakan confusion matrix. Data yang digunakan dalam penelitian ini sejumlah 15000 data yang terbagi menjadi 3 kelas dengan masing-masing kelas berjumlah 5000 data. Pada penelitian ini parameter uji coba yang digunakan yaitu probabilitas dropout dan jumlah batchsize. Lalu, metode reduksi fitur yang digunakan yaitu PCA. Hasil terbaik yang diperoleh yaitu pada pembagian data 90:10 dengan nilai probabilitas dropout 0.9 dan jumlah batchsize 8 dengan memperoleh nilai akurasi, sensitivitas, spesifitas berturur-turut yaitu 99.95%, 99.97%, dan 99.86% serta membutuhkan waktu training selama 93 menit 27 detik.
Classification of Cumulonimbus Cloud Formation based on Himawari Images using Convolutional Neural Network model Googlenet Mohammad Rizal Abidin; Dian candra Rini Novitasari; Hani Khaulasari; Fajar Setiawan
Jurnal Buana Informatika Vol. 14 No. 02 (2023): Jurnal Buana Informatika, Volume 14, Nomor 2, Oktober 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i02.7417

Abstract

Cumulonimbus clouds (Cb) are dangerous for many human activities. To reduce this effect, a system to classify formations is needed. The formation of Cb clouds can be seen in the Himawari-8 IR image. This research aimed to create a Cb cloud classification system with Himawari-8 IR Enhanced imagery using the GoogleNet model CNN method. The total data used was 2026 image data. Parameter testing was carried out on the CNN GoogleNet model in this study, namely a data distribution ratio of 90:10 and 80:20. The probability of dropout is 0.6, 0.7, and 0.8. and batch sizes of 8, 16, 32, and 64. The trials conducted in this study yielded a sensitivity value of 100.00%, an accuracy of 99.00%, and a specificity of 99.60% obtained from the experimental data distribution of 90:10, probability 0.8, and batch size 8.
Co-Authors Abdulloh Hamid Abdulloh Hamid Achmad Teguh Wibowo Adam Fahmi Khariri Adyanti, Deasy Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Zoebad Foeady Ahmad Zoebad Foeady Aisyah, Nora Alvin Nuralif Ramadanti Amin, Faris Mushlihul Arifin, Ahmad Zaenal Aris Fanani Ariyanto Wijaya, Indra Ariyanto, Dimas Azmi, Tasya Auliya Ulul Chalawatul Ais Damayanti, Adelia Deasy Adyanti Dianita Utami, Wika Dilla Dwi Kartika Diva Ayu Safitri Nur Maghfiroh Elen Riswana Safila Putri Fahriza Novianti Fajar Setiawan Fajar Setiawan Fajar Setiawan Fajar Setiawan FAJAR SETIAWAN Fanani, Aris Farida, Yuniar Faris Mushlihul Amin Farmita, Mayandah Ferryan, Dhandy Ahmad Firmansjah, Muhammad Fitria, Nur Annisa Foeady, Ahmad Zoebad Galuh Andriani Ganeshar B.D. Prasanda Gita Purnamasari R Hani Khaulasari Hanimatim Mu'jizah Haq, Dina Zatusiva Ifadah, Corii Indriyani, Jiphie Gilia Irkhana Indaka Zulfa Jauharotul Inayah Kurniawan, Mohammad Lail Kusaeri Kusaeri Lubab, Ahmad Luluk Mahfiroh Lutfi Hakim Lutfi Hakim Lutfi Hakim Luthfi Hakim Luthfi Hakim M. Hasan Bisri Mardiyah, Ilmiatul Masruroh Kusman, Umi Maulana, Achmad Resnu Maulana, Jeneiro Moh. Hafiyusholeh Mohammad Rizal Abidin Mohd Fauzi, Shukor Sanim Monika Refiana Nurfadila Muhammad Fahrur Rozi MUHAMMAD FAHRUR ROZI Muhammad Syaifulloh Fattah Muhammad Thohir Musfiroh Musfiroh, Musfiroh Nanang Widodo Nanang Widodo Nanang Widodo Nanang Widodo Nisa Trianifa Noviati Maharani Sunariadi Noviati Maharani Sunariadi Nur Afifah Nur Hidayah Nurissaidah Ulinnuha Pramesti, Diah Devi Puspitasari, Wahyu Tri Putri Wulandari Putri, Evi Septya Putroue Keumala Intan Rafika Veriani Ramadanti, Alvin Nuralif Ratnasari, Cristanti Dwi Rifa Atul Hasanah RIFA ATUL HASANAH Rozi, Muhammad Fahrur Rozzy, Fahrul Safira, Icha Dwi Sani, Puteri Permata Sari, Firda Yunita Sari, Ghaluh Indah Permata Sari, Yana Vita Setiawan, Fajar Setyawati, Maunah Siti Nur Aisah Siti Nur Fadilah Siti Nur Fadilah Siti Ria Riqmawatin Sukarni, Adinda Ika Sulistiya Nengse Sulistiyawati, Dewi Suwanto Suwanto Suwanto Suwanto Swindiarto, Victory T. Pambudi Tasya Auliya Ulul Azmi Unix Izyah Arfianti USWATUN KHASANAH Utami, Tri Mar'ati Nur Utami, Wika Dianita Utami Dianita Veriani, Rafika Vina Fitriyana Wanda N.P. Sunaryo Wijaya, Indra Ariyanto Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wisnawa, Gede Gangga Yasirah Rezqita Aisyah Yasmin Yuliati, Dian Yuliawanti, Felia Dria Yuni Hariningsih Yuniar Farida, Yuniar Yusuf, Ahmad Yuyun Monita Yuyun Monita Zahroh, Khofifah Auliyatuz Zulfa, Elok Indana