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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Simantec Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komtika (Komputasi dan Informatika) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi CSRID (Computer Science Research and Its Development Journal) Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Informasi dan Teknologi JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Walisongo Journal of Information Technology Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK TIERS Information Technology Journal Jurnal Informatika Dan Tekonologi Komputer (JITEK) Adpebi Science Series
Finki Dona Marleny
Universitas Muhammadiyah Banjarmasin
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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RANCANG BANGUN PROTOTIPE SEPEDA AIR CERDAS PEMANTAUAN SAMPAH BERBASIS IOT Finki Marleny; Finki Dona Marleny
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 6 No. 2 (2021)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v6i2.64

Abstract

Banjarmasin terkenal dengan julukannya sebagai kota seribu sungai, sebagian besar masyarakat memanfaatkan sungai untuk kehidupan sehari-hari. Secara geografis, Kota Banjarmasin memiliki luas wilayah sekitar 98,46 kilometer persegi, kota ini memiliki banyak sungai yang membelah antara satu daratan dengan daratan yang lain. Permasalahan klasik di daerah sungai yaitu seperti sampah batang kayu, bambu, eceng gondok hingga sampah plastik limbah rumah tangga yang sering menumpuk di beberapa kolong jembatan dan pinggiran sungai di Banjarmasin menjadi perhatian untuk kelestarian ling-kungan. Berdasarkan permasalahan tersebut maka penelitian ini bertujuan untuk mengembangkan sistem pemantauan sam-pah di daerah sungai yaitu berupa Rancangan arsitektur Prototipe Sepeda Air Cerdas untuk Pemantauan Sampah Berbasis IoT(Internet of Things), dimana pada tahapan metode penelitian ini menggunakan prototipe sepeda air yang menggunakan sensor-sensor cerdas untuk memantau sampah-sampah yang ada di daerah sungai kemudian sensor dapat mengenali sampah sehingga dapat terdata dan segera terhubung diaplikasi seluler. Pada tahapan ini adalah tahapan pemodelan visual dan arsitektur dari pengembangan prototipe sepeda cerdas berbasis IoT. Hasil pada penelitian ini adalah gambaran pengem-bangan arsitektur sistem prototipe sepeda air cerdas sebagai cetak biru untuk pengembangan lebih lanjut pada tahap pengem-bangan perangkat. Gambaran cetak biru dari penelitian ini dapat menjadi rujukan untuk pengembangan sistem berbasis IoT dalam sistem monitoring pemantauan sampah di daerah sungai.
Explanatory Data Analysis to Evaluate Keyword Searches for Educational Videos on YouTube with a Machine Learning Approach Mambang Mambang; Ahmad Hidayat; Johan Wahyudi; Finki Dona Marleny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11502

Abstract

One of the most important parts of data science is the process of explanatory data analysis. This study aims to analyze learning videos on YouTube using search keywords such as learning biology, chemistry, physics, computers, mathematics, management, accounting, citizenship, history, and culture. The method used is the explanatory data analysis technique with a Machine Learning approach. The dataset used in this study uses learning video search keywords found on the YouTube digital platform. After doing a thorough analysis of all existing variables, we found that in the context of searching for learning video keywords on YouTube, the viewing variable has a heatmap correlation of 0.97 on the likes variable, 0.97 on the subscribers variable, -0.15 on the duration variable and 0.95 on the comment variable. The duration variable negatively correlates with all variables based on the analysis using a correlation heatmap using the seaborn library. Our analysis found that the number of learning videos with the search keyword Mathematics had the highest number of views among other variables. Further research can use existing variables or also add variables and add search keywords on YouTube. The data analysis approach can also be done using SPSS, R and also a Machine Learning approach with different libraries.
EXPLANATORY DATA ANALISIS UNTUK MENGEVALUASI PENELUSURAN KATA KUNCI VIDEO PEMBELAJARAN DI YOUTUBE DENGAN PENDEKATAN MACHINE LEARNING Mambang Mambang; Ahmad Hidayat; Finki Dona Marleny; Johan Wahyudi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 2 No. 2 (2022): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : AMIK Veteran Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v2i2.287

Abstract

The purpose of this study was to find correlations related to the variable number of impressions, likes, subscribers, and comments on each learning video keyword search on YouTube. This research uses quantitative methods and experiments with secondary data sources. Exploratory Data Analysis in machine learning using several libraries in Python programming produces image visualizations that provide information related to the dataset that has been processed, such as boxplot graphs, histograms, line plots, and correlation graphs. Exploratory Data Analysis with machine learning that we have done finds results on boxplot graphs on five variables showing a whisker more elongated upwards which states positive data results. The difference in this histogram chart is in the duration variable. On the line plot graph, we find the keywords learning videos learning mathematics have the advantage of four variables and the keywords of accounting learning videos one variable. Exploratory Data Analysis using the correlation head map in the seaborn library shows that the like and comment variables strongly correlate with a value of 1. Duration variables have a low and negative correlation with other variables. The subscribers variable has a high correlation with the like variable 0.95. Thus, several indicators need to be considered in making learning videos, such as content or content of innovative and creative learning videos, so that the number of likes and comments becomes high. The length of time in learning videos does not affect the number of likes, subscribers, and comments.
Deteksi Titik Kebakaran Lahan Menggunakan Wireless Sensor Network Kamarudin Kamarudin; Muhammad Ziki Elfirman; Ihdalhubbi Maulida; Finki Dona Marleny; Rudy Ansari; Maman Fatahulrahman
Jurnal Komtika (Komputasi dan Informatika) Vol 6 No 1 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v6i1.6264

Abstract

Kalimantan Tengah merupakan salah satu propinsi yang rawan terjadi kebakaran lahan dan hutan di Indonesia yang memiliki puncak tertinggi kasus kebakaran lahan seluas 317.749 Ha di tahun 2019. Berbagai upaya telah dilakukan oleh pemerintah daearah untuk mencegah terjadinya kebakaran lahan, baik disebabkan oleh alam atau manusia. Penelitian ini dilakukan dengan membuat alat deteksi dini titik kebakaran lahan dengan memanfaatkan 4 sensor, yaitu Soil Sensor (sensor kelembaban tanah), DHT11 (sensor suhu dan kelembaban), Flame Sensor (sesnsor api), dan MQ2 (sensor asap). Pengujian alat dilaksanakan di salah satu kecamatan yang memiliki potensi kebakaran lahan yang tinggi, yaitu di Kecamatan Kumai Kabupaten Kotawaringin Barat. Alat yang diuji akan mengirimkan hasil deteksi ke server web melalui sim 800L, yang kemudian diolah datanya untuk menghasilkan informasi yang ditampilkan ke halaman web, hingga dapat diambil tindakan dini oleh masyarakat dalam pencegahan kebakaran lahan.
Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach Finki Dona Marleny; Mambang Mambang
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.488 KB) | DOI: 10.38043/tiers.v3i1.3604

Abstract

Floods and abrasion are the most common disasters in Indonesia. A lot of data is collected from post-flood and abrasion disasters. From the data released by BNPB, disaster data is directly based on the occurrence of disasters. From these data, we will test predictive modeling classification with a deep learning approach. Big data can be made through classification and predictive modeling. Our proposed model is a classification of predictive modeling of post-flood and abrasion data using the H2O deep learning approach. Deep learning H2O models can also be evaluated with specific model metrics, termination metrics, and performance charts. This approach is used to optimize the performance and accuracy of predictions during the modeling process using our dataset pool training. The big data to be processed will generate new knowledge for policies in decision making. Big data performance modeled with Deep Learning H2O is used to predict the Survival attributes of post-flood and abrasion sample datasets. The best metric performance is obtained from the maxout activation function with a 200-200 unit layer that gets an accuracy of 93.49% with AUC: 0.808 +/- 0.022 (micro average: 0.808).
Evaluasi Maturity Level Tata Kelola Teknologi Informasi di Perpustakaan Perguruan Tinggi Menggunakan Cobit 5 Mambang Mambang; Finki Dona Marleny; Septyan Eka Prastya; Muhammad Zulfadhilah; Subhan Panji Cipta; Jaya Hari Santoso; Miranda Miranda; M Samsul Hasmi; M Samsul Hasbi
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4546

Abstract

Abstrak- Solusi teknologi informasi harus diidentifikasi atau layanan yang diimplementasikan dan diamanatkan termasuk penyediaan layanan, manajemen keamanan dan kontinuitas, layanan dukungan  pengguna, manajemen data, dan fasilitas operasi. Untuk mengukur tingkat kematangan perpustakaan di lakukan audit tata kelola teknologi informasi untuk mengevaluasi, menganalisis, mengawasi, kepatuhan regulasi teknologi informasi apakah dalam manajemen risiko dalam perpustakaan layak digunakan atau tidak layak digunakan di perpustakaan. Metode dalam mendapatkan data dan informasi dari proses tata kelola TI pada Perpustakaan XYZ dengan kuesioner dan wawancara langsung. Berdasarkan rata - rata nilai maturity level di semua domain proses yang dilakukan audit dengan kerangka kerja COBIT 5 berada pada nilai 3,3 atau pada level 3 (Established). Pada level ini secara keseluruhan proses standar didefinisikan dan digunakan di seluruh organisasi. Rata-rata gap analisis berada pada nilai 1,63 yang menunjukan proses Tata Kelola IT di Perpustakaan XYZ sudah berjalan dengan baik. Semakin kecil nilai rata-rata gap analisis dengan nilai ekspektasi maka semakin bagus bagi pengeloaan TI pada sebuah Instansi, perusahaan dan bidang industry lainnya yang menggunakan infrastuktur IT baik pada pengelolaan perangkat keras, perangkat lunak dan pengelolaan sumber daya manusia. Untuk penelitian selanjutnya, bisa menambahkan lebih banyak lagi domain proses baik pada area Tata Kelola (Governance) dan area Manajemen (Management), sehingga proses audit dengan kerangka kerja COBIT 5 dapat dilakukan dengan komprehensif.Kata kunci: Maturity Level, Tata Kelola Teknologi Informasi, Perpustakaan, COBIT 5 Abstract- Information technology solutions should be identified or services implemented and mandated, including service provision, security and continuity management, user support services, data management, and operations facilities. Methods of obtaining data and information from the IT governance process at the XYZ Library with questionnaires and direct interviews. To measure the library's maturity level, an information technology governance audit is carried out to evaluate, analyze, supervise, and comply with information technology regulations whether in risk management in the library is feasible to use or unfit for use in the library. The average maturity level value in all process domains audited with the COBIT 5 framework is 3.3 or at level 3 (Established). The overall standard process is defined and used throughout the organization at this level. The average analysis gap is at a value of 1.63, which shows that the IT Governance process in the XYZ Library is already running well. The smaller the average value of the analysis gap with the expectation value, the better it is for IT management in an agency, company and other industrial fields that use IT infrastructure in hardware management, software and human resource management. For further research, we can add more process domains both in the Governance and Management areas so that the audit process with the COBIT 5 framework can be carried out comprehensively.Keywords: Maturity Level, Information Technology Governance, Library, COBIT 5
Evaluasi Tata Kelola Teknologi Informasi di Perpustakaan Perguruan Tinggi dengan COBIT 5 Mambang; Finki Dona Marleny; Wulandari Febriani; Theresia Kurniati Seran; Nalo Valentino
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i3.209

Abstract

The COBIT 5 framework can be implemented in all organizations or enterprises. This study analyzes and finds the maturity level of IT governance in the XYZ Library. Libraries have a role in encouraging the efficiency and effectiveness of the learning process. The IT governance element in COBIT 5 aims to get results from evaluating stakeholders' needs, conditions and choices. Survey and in-person interviews are methods of obtaining data and information from the IT governance process at the XYZ Library. The COBIT 5 domains used are evaluated, direct, and monitored (EDM) and align, plan and organize (APO) domains. The results of the evaluation that have been carried out show the findings of gaps in the process domain EDM01 with a value of 2.34 and EDM04 with a gap of 2.25. While in the process domain, APO01 with a gap of 2.13, and APO07 with a gap of 2.34. With the average value of the entire gap at the level of 2.26, the findings of the XYZ Library with the process domains EDM01, EDM04, APO01, and APO07 show that the process is managed and the results are determined, controlled, and maintained (Managed). For further research, we can add more process domains both in the Governance and Management areas so that the audit process with the COBIT 5 framework can be carried out comprehensively.
Exploratory Data Analysis of Exact Science and Social Science Learning Content on Digital Platform Mambang - Mambang
Walisongo Journal of Information Technology Vol 4, No 2 (2022)
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2022.4.2.12676

Abstract

Data is one of the essential aspects in providing new information and new knowledge so that the data exploration process can provide policies on a decision for many sectors. Exploratory Data Analysis in this paper begins with collecting datasets contained on the Youtube digital platform. The dataset used was 30 samples found on the top page of youtube in each keyword. After conducting the Exploratory Data Analysis process, we found new learning content on the digital youtube platform. From the Exploratory Data Analysis that has been carried out, we also find different variations of the analysis's variables. The duration variable shows the result that the total duration of the overall duration in mathematics learning content that includes the Exact Science field is less than the psychology learning content included in the Social Science field. Meanwhile, the overall number of views on mathematics learning content is more than the number of views on psychology learning content. From the collecting dataset that we have made, showing a considerable number of views is undoubtedly the key to equitable distribution of information and knowledge for all users. More innovation and creating learning content are expected to encourage increased human development.
PADDY WETLAND PRODUCTIVITY ANALYSIS WITH LINEAR REGRESSION OF MACHINE LEARNING APPROACH Bayu Nugraha; Agustina Hotma Uli Tumanggor; Mambang; Finki Dona Marleny
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 2 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i2.138

Abstract

Paddy is one of the priority crops in agricultural production. South Kalimantan is an area that produces Paddy. In paddy productivity in the southern Kalimantan region, there are paddy wetlands and paddy dryland. The need for paddy production in the southern Kalimantan region can increase or decrease every year. The method used in this study is a linear regression algorithm with a machine learning approach. Linear regression analysis basically predicts a variable's value based on its free variables. Linear regression only predicts variables whose data nature is intervals or ratios. Linear regression analysis can be used to examine the relationship between two or more variables. Linear regression can also make additional assumptions between variables through the most suitable lines of straight-line data points. This study is to determine the relationship between harvest area and productivity. As a result of trials using the machine learning approach, linear regression algorithms show a relationship between harvest and production area. The correlation test results can find relationships between data points so that linear regression can be used to predict. From the relationship between harvest area and productivity, a prediction accuracy of 95% was obtained.
Prediction of linear model on stunting prevalence with machine learning approach Mambang Mambang; Finki Dona Marleny; Muhammad Zulfadhilah
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4028

Abstract

An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.
Co-Authors Ade Putri Maharani Agus Byna Ahmad Faisal Hamidi Ahmad Hidayat Ahmad Riki Renaldy Akhmad Baddrudin Antonia Yenitia Astia Rahma Tiara Aulia Fitri Aulia Fitri Ayu Ahadi Ningrum Bambang Lareno, Bambang Bayu Nugraha Bima Wicaksono Dixky Dixky Elisa Fitriana Fitriansyah, Muhammad Haniffah Sri Rinjani Hudatul Aulia Ihdalhubbi Maulida Ihsanudin Indah Wulandari Jaya Hari Santoso Johan Wahyudi Kamarudin Kartika Kartika Liliana Swastina M Samsul Hasbi M Samsul Hasmi Maman Fatahulrahman Mambang Mambang Fitriansyah Mambang Samita Maria Ulfah Maulida, Ihdalhubbi Meila Izzana, Meila Melda Melda Miranda Miranda Muhammad Khairul Akbar Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Zaini Bakri Muhammad Ziki Elfirman Muhammad Ziki Elfirman Muhammad Zulfadhilah Mukhaimy Gazali Mutmainah Mutmainah Nalo Valentino Nor Azizah Nur Hafiz Ansari Nur Meilianti Maulida Nurhaeni Nurhaeni Prastya, Septyan Eka Putri Putri Rahmini Rahmini Reni Emiliya Ricardus A P, Ricardus A Risma Maulida Risma Risma Rismawati Rismawati Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari Rudy Ansari Sandro Nesta Pembriano Sa’adah Sa’adah Septian Eka Prastya Septyan Eka Prastya Subhan Panji Cipta Tasya Salsabila Theresia Kurniati Seran Tumanggor, Agustina Hotma Uli Winda Astria Nuansa Saputri Winda Astria Nuansa Saputri Windarsyah Windarsyah Wulandari Febriani Yulisa Suryana Yuslena Sari, Yuslena