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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 Buana Informatika Perfecting a Video Game with Game Metrics Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics 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 JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) 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)
Dian Candra Rini, Dian Candra
Universitas Islam Negeri Sunan Ampel
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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OPTIMASI MODEL PENUGASAN BERDASARKAN PERAMALAN LAYANAN KAPAL TUNDA DI PELABUHAN TANJUNG PERAK MENGGUNAKAN METODE BACKPROPAGATION Umi Masruroh Kusman; Abdulloh Hamid; Dian Candra Rini Novitasari; Wika Dianita Utami; Indra Ariyanto Wijaya
Jurnal Mnemonic Vol 6 No 1 (2023): Mnemonic Vol. 6 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v6i1.6008

Abstract

Indonesia merupakan negara kepulauan terbesar di dunia. Pemanfaatan laut yang optimal berpotensi meningkatkan perekonomian negara. Pelabuhan Tanjung Perak adalah pelabuhan terbesar dan tersibuk kedua di Indonesia. Efisiensi penggunaan kapal tunda berpengaruh signifikan terhadap operasional pelabuhan. Penelitian ini mengusulkan metode backpropagation untuk melakukan peramalan permintaan pelayanan kapal tunda, kemudian hasil peramalan dimasukkan ke dalam model penugasan untuk mengetahui optimalisasi penggunaan kapal tunda berdasarkan tingkat kesibukan kapal tunda, waktu tunggu layanan, dan jumlah antrian. Data yang digunakan dalam penelitian ini adalah data sekunder, yaitu data permintaan pelayanan kapal tunda pada bulan Januari 2019 – Mei 2022 yang dibedakan menjadi tiga, yaitu permintaan kapal kecil, sedang, dan besar. Hasil peramalan menggunakan metode backpropagation menghasilkan nilai MAPE sangat baik dibawah 10%. permintaan pelayanan terbanyak oleh kapal kecil dan besar terjadi pada bulan Juni dengan masing-masing sebanyak 2215 dan 51 permintaan, kemudian untuk kapal sedang permintaan terbanyak terjadi pada bulan Januari dengan jumlah permintaan mencapai 451. Sedangkan, permintaan terkecil pada kapal kecil terjadi pada bulan Februari dan September dengan jumlah permintaan 2141, kemudian permintaan terkecil dari kapal sedang terjadi pada bulan Juli dengan permintaan sebanyak 421, dan permintaan terkecil pada kapal besar terjadi pada bulan Januari dengan jumlah permintaan sebanyak 47. Hasil penugasan pada kapal tunda mencapai tingkat optimal dengan mengoperasikan 13 kapal tunda setiap harinya.
Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian Musfiroh Musfiroh; Dian Candra Rini Novitasari; Putroue Keumala Intan; Gede Gangga Wisnawa
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3114

Abstract

Since the last three years North Luwu has experienced frequent hydrological disasters in the form of floods and landslides. The disaster had a negative impact on the availability of clean water, failed to plant and even tended to reduce the quality of the harvest. Cocoa is one of the leading commodities of North Luwu Regency whose productivity has decreased due to the impact of climate change so that it will affect the sustainability of the local population's income. Therefore, the purpose of this research is to anticipate rainfall that will occur to prevent or reduce the risk of failure and loss. Principal Component Analysis (PCA) Method is used as feature extraction to find out the most influential variables and the Long Short-Term Memory (LSTM) method is used as a prediction method. Future rainfall is predicted using meteorological variables such as pressure, evaporation, maximum temperature, average humidity, and sunshine duration from 1 January 2017 to 30 September 2022. Based on the PCA results, 4 variables are obtained that have the most influence on rainfall, namely: variable evaporation, maximum temperature, average humidity, and length of sunlight. These variables are used as input to predict rainfall using LSTM. In this study using trial parameters, namely the number of hidden, batch size, and learn rate drop period. The best prediction results were obtained for MAPE of 0.0018 with the number of hidden, batch size and learn rate drop periods of 100, 32, and 50 respectively. The prediction results show very heavy rainfall occurring on August 28, 2021 of 101.9734 mm, 21 September 2021 of 108.6528 mm, and 5 April 2022 of 116.5510 mm. In this study PCA was able to increase accuracy in considering all parameters and choosing the most effective.
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 Adam Fahmi Khariri Adelia Damayanti Adyanti, Deasy Ahmad Hanif Asyhar Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Lubab Ahmad Yusuf Ahmad Zoebad Foeady Ahmad Zoebad Foeady Alvin Nuralif Ramadanti Arifin, Ahmad Zaenal Aris Fanani Aris Fanani Chalawatul Ais Deasy Adyanti Dewi Sulistiyawati Dilla Dwi Kartika Dina Zatusiva Haq Dina Zatusiva Haq Diva Ayu Safitri Nur Maghfiroh Elen Riswana Safila Putri Evi Septya Putri Fahriza Novianti Fajar Setiawan Fajar Setiawan Fajar Setiawan FAJAR SETIAWAN Fajar Setiawan Faris Mushlihul Amin Ferryan, Dhandy Ahmad Firmansjah, Muhammad Foeady, Ahmad Zoebad Galuh Andriani Ganeshar B.D. Prasanda Gede Gangga Wisnawa Gita Purnamasari R Hani Khaulasari Hanimatim Mu'jizah Ifadah, Corii Ilmiatul Mardiyah Indra Ariyanto Wijaya Irkhana Indaka Zulfa Jauharotul Inayah Kusaeri Kusaeri Luluk Mahfiroh Lutfi Hakim Lutfi Hakim Luthfi Hakim M. Hasan Bisri Mayandah Farmita Moh. Hafiyusholeh Moh. Hafiyusholeh Moh. Hafiyusholeh Moh. Hafiyusholeh Mohammad Lail Kurniawan Mohammad Rizal Abidin Monika Refiana Nurfadila MUHAMMAD FAHRUR ROZI Muhammad Fahrur Rozi Muhammad Syaifulloh Fattah Muhammad Thohir Musfiroh Musfiroh Nanang Widodo Nanang Widodo Nanang Widodo Nisa Trianifa Noviati Maharani Sunariadi Noviati Maharani Sunariadi Nur Afifah Nur Hidayah Nurissaidah Ulinnuha Nurissaidah Ulinnuha Nurissaidah Ulinnuha Putri Wulandari Putroue Keumala Intan Putroue Keumala Intan Putroue Keumala Intan Putroue Keumala Intan Rafika Veriani Ratnasari, Cristanti Dwi RIFA ATUL HASANAH Rifa Atul Hasanah Rozi, Muhammad Fahrur Sari, Ghaluh Indah Permata Setiawan, Fajar Siti Nur Fadilah Siti Nur Fadilah Siti Ria Riqmawatin Suwanto Suwanto Suwanto Suwanto Tasya Auliya Ulul Azmi Thohir, Muhammad Ulinnuha, Nurissaidah Umi Masruroh Kusman Unix Izyah Arfianti USWATUN KHASANAH Utami, Tri Mar'ati Nur Veriani, Rafika Vina Fitriyana Wanda N.P. Sunaryo Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Dianita Utami Yasirah Rezqita Aisyah Yasmin Yuni Hariningsih Yuniar Farida Yuniar Farida, Yuniar Yuyun Monita Yuyun Monita Zulfa, Elok Indana