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Ramadhani Ramadhani
Prodi S1 Ekonomi Pembangunan USU
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Analisis Kepuasan Masyarakat Terhadap Kinerja Bupati Labuhanbatu Selatan Periode 2021-2024 Menggunakan Metode Decision Tree dan Naive Bayes Ramadhani, Ramadhani; Harahap, Syaiful Zuhri; Suryadi, Sudi; Masrizal, Masrizal
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7971

Abstract

This study was conducted to analyze the level of customer satisfaction with services by comparing the performance of two classification methods, namely Decision Tree and Naive Bayes, so that an accurate model can be obtained to assist decision making. This problem is important because understanding customer satisfaction patterns can be a strategic basis in improving service quality and maintaining loyalty. The theoretical basis used refers to the concept of machine learning classification, where Decision Tree forms a branching rule-based model based on attributes, while Naive Bayes relies on probability calculations based on Bayes' theorem with the assumption of independence between features. The research methodology includes data collection stages, pre-processing to ensure data quality, model training with both methods, and performance evaluation using Test & Score and Confusion Matrix. Based on the classification results, the Decision Tree method produces fairly good accuracy, precision, and recall, but the Naive Bayes method shows higher performance with an accuracy of 91.67%, a precision of the "Satisfied" class of 98.11%, and a recall of 92.86%, which indicates a very good level of prediction accuracy especially for the majority class. Evaluation of both methods shows that Naive Bayes excels in capturing existing data patterns, although Decision Tree still has good interpretability for classification rule analysis. In conclusion, both methods are capable of classifying customer satisfaction data with adequate performance, but Naive Bayes is recommended as the primary model due to its higher and more consistent evaluation results, while Decision Tree can be used as an alternative when model interpretation is a priority.
PENINGKATAN KEMAMPUAN PEMECAHAN MASALAH MATEMATIS DAN MINAT BELAJAR SISWA DENGAN MODEL PBL BERBASIS ETNOMATEMATIKA Lutfitasari, Devi; Nasution, Haryati Ahda; Ramadhani, Ramadhani
SIGMA Vol 11, No 1 (2025): SIGMA
Publisher : Prodi Pendidikan Matematika FKIP Universitas Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53712/sigma.v11i1.2710

Abstract

Penelitian ini bertujuan untuk meningkatkan kemampuan memecahkan masalah matematis dan minat belajar siswa melalui model pembelajaran PBL berbasis etnomatematika pada materi bangun datar. Metode pada penelitian ini adalah kuantitatif dengan menggunakan true exsperimenal dimana dalam penelitian ini diambil dua kel;as yaitu kelas eksperimen dan kelas titit sebagai perbandingan. Penelitian ini menggunakan uji-t sebagai uji instrumen penelitian, hasil uji-t menunjukkan thitung > t tabel pada tingkat signifikansi 5% atau 0,05 (3,464 > 2,008), sehingga hipotesis alternatif H a diterima dan H 0 ditolak. Dengan demikian, dapat disimpulkan bahwa dalam penelitian ini mengindikasikan adanya peningkatan kemampuan siswa dalam menyelesaikan masalah matematika pada mareri bangun datar melalui penerapan model Problem Based Learning yang berbasis etnomatematika.
Emotional Intelligence in Islamic Education Abidin, Zainul; Dafrizal, Dafrizal; Ramadhani, Ramadhani
Suluah Pasaman Vol 3 No 1 (2025): April
Publisher : Sekolah Tinggi Agama Islam YDI Lubuk Sikaping

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70588/suluahpasaman.v3i1.584

Abstract

One important component for being able to live in society is the ability to manage emotions well. Research conducted by Goleman shows that intelligence quotient contributes only about twenty percent to a person's success, with the remaining eighty percent determined by a group of factors called emotional intelligence. The reality today is that a high intelligence quotient does not necessarily lead to success or a happy life. People who are highly intelligent but have unstable emotions and are easily offended often make mistakes in determining and solving life problems because they cannot concentrate. Their emotions are undeveloped, unburdened, and they often change when facing problems and behave towards others in a way that causes a lot of conflict. Poorly managed emotions also make it easy for others to enthusiastically agree to something, but then quickly change their minds and refuse, thereby disrupting the cooperation that has been agreed upon with others. Thus, the man fails. Islamic education pays close attention to this issue. This can be seen in the task of Islamic education, which is to guide and direct the growth and development of human beings from stage to stage of the students' lives in order to achieve optimal performance.
Human development index, geographic disparities and strategies to reduce maternal mortality in Indonesia : an ecological study Ramadhani, Ramadhani; Tri Yunis Miko Wahyono
Jurnal Kesehatan Ibu dan Anak Vol. 17 No. 2
Publisher : Poltekkes Kemenkes Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29238/kia.v17i2.2051

Abstract

Recent data shows disparities and setbacks in maternal health services. In Indonesia, there was an increase in maternal mortality in 2020 compared to 2019. Inequality is a powerful predictor of maternal mortality. This study aims to find whether inequality indicators, namely the Human Development Index and Geographic Units, can be predictors of maternal mortality. This research uses an ecological study design with a unit of analysis in 34 provinces in Indonesia. The data used in this study is secondary data from the Ministry of Health and Statistics Indonesia. Descriptive analysis and Poisson regression were used to determine whether the Human Development Index and geographic units could be predictors of maternal mortality. The increase in maternal mortality occurred in 21 (61%) provinces in Indonesia. The province's lowest maternal mortality rate was 48, and the highest was 565 per 100,000 live births. The Human Development Index and geographic units can significantly predict maternal mortality (P < 0.05). Human development indices and geographic units are significant predictors of maternal mortality. Strategies that can be done to reduce maternal mortality are improved education, health services, and specific and collaborative interventions according to provincial needs.
Karakteristik Marshall Campuran AC–WC yang Menggunakan Abu Tempurung Kelapa dan Abu Cangkang Sawit Sebagai Pengganti Filler Putra, Rio Syaenanda; Ramadhani, Ramadhani; Haruno, Hardayani
Jurnal Multidisiplin Dehasen (MUDE) Vol 4 No 4 (2025): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/mude.v4i4.9328

Abstract

This research discusses the utilization of palm kernel shell ash and coconut shell ash as filler substitutes in Asphalt Concrete - Wearing Course (AC - WC) mixtures. This research uses a laboratory test method, which is to find the results of using palm kernel shell ash and coconut shell ash as filler additives in the Asphalt Concrete - Wearing Course (AC - WC) layer. This research was conducted through the Marshall test to determine the stability and flow (melt) values of the test samples. Marshall properties refer to the maximum load that an asphalt mixture can withstand, even at its melting point. The flow rate is expressed in kilograms. Marshall parameters include VIM (Void in Mix), research-related data collection in the form of references and supporting equipment to study asphalt pavement mixtures to study the effect of temperature changes. The Marshall test research was then continued by collecting information about the materials used in the laboratory experiments used in the test specimens. VFA (Void Filled With Asphalt), VMA (Void In Mineral Aggregate), Stability, Flow and Marshall Quotien (MQ) values.
Analisa Tampungan Drainase Pengendalian Banjir Sungai Lambidaro - Sekanak Kota Palembang (Studi Kasus Jembatan Letnan Mukmin - Jembatan Tua Patih Naya Raya) Junaina, Eka; Sahbar, Robi; Ramadhani, Ramadhani
Jurnal Multidisiplin Dehasen (MUDE) Vol 4 No 4 (2025): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/mude.v4i4.9391

Abstract

Population growth in Palembang City has put pressure on the fulfillment of housing needs, industrial/service areas and supporting facilities which have caused land that was previously a conservation area, water catchment area, to turn into watertight areas which have caused increased water flow from an area which causes puddles and floods which tend to increase over time, coupled with the geographical conditions of Palembang City with rainfall during 2022 ranging from 97.2 mm (July) to 47.30 mm (January), and has relatively high air humidity where the average ranges from 81.00% to 84.30% in March so that Palembang City is highly likely to experience frequent rain. There are 19 (nineteen) flood control drainage systems in Palembang City, one of which is the Lambidaro and Sekanak river drainage subsystems. The purpose of this study was to determine the storage capacity of the Lambidaro-Sekanak River flood control drainage channel for the next 5-year return period.The required data included the existing condition of the Lambidaro-Sekanak River flood control drainage channel, rainfall data, and other specifications related to the test results.The analysis revealed that the drainage capacity for the 5-year return period was 10,116 m3/s
Pengaruh Media Pembelajaran Powerpoint dan Model Problem Based Learning Terhadap Motivasi dan Kemampuan Penalaran Matematika Syafitri, Linda Evi; Ramadhani, Ramadhani; Rangkuti, Darmina Eka Sari
MUDABBIR Journal Research and Education Studies Vol. 5 No. 2 (2025): Vol. 5 No. 2 Juli-Desember 2025
Publisher : Perkumpulan Manajer Pendidikan Islam Indonesia (PERMAPENDIS) Prov. Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56832/mudabbir.v5i2.1632

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh media powerpoint dan model problem based learning terhadap motivasi belajar dan kemampuan penalaran matematis siswa. Penelitian ini merupakan penelitian Studi literature dengan pendekatan kualitatif dengan sumber data berupa penelitian terdahulu. Penelitian ini menganalisis 12 artikel terkait dengan motivasi belajar siswa dan kemampuan penalaran matematis siswa, berdasarkan penggunaan media powerpoint dan model PBL. Teknik pengumpulan data yang digunakan adalah Studi literature . hasil penelitian menunjukkan : (1) Adanya pengaruh Media powerpoint terhadap motivasi belajar dan kemampuan penalaran matematis siswa. Hal ini dapat dilihat dari nilai post-test yang lebih tinggi dibandingkan dengan nilai pre-test siswa dari beberapa artikel. (2) Adanya pengaruh Model problem based learning terhadap motivasi belajar dan kemampuan penalaran matematis siswa. Hal ini dapat dilihat dari nilai post-test yang lebih tinggi dibandingkan dengan nilai pre-test siswa dari beberapa artikel. Namun tidak semua siswa dapat pengaruhdari penggunaan model ini.
Analisis Dampak Penggunaan Gadget Terhadap Konsentrasi Belajar Mahasiswa Sekolah Tinggi Ekonomi Balikpapan Afiifah, Nailah Nur; Arthamivera, Meica; Mardianto, Putri Sekar; Ramadhani, Ramadhani; Rahmah, Rahmah; anhar, Anhar
Indo-MathEdu Intellectuals Journal Vol. 6 No. 6 (2025): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v6i6.4340

Abstract

This study aims to determine the impact of gadget use on the learning concentration of students at the Balikpapan College of Economics. The research method used is quantitative with a survey design. The sample consisted of 150 students selected by purposive sampling from various departments. The research instrument was a questionnaire that measured the intensity of gadget use, types of gadget activities (academic and non-academic), and level of learning concentration. Data analysis was performed using multiple linear regression. The research instrument was a questionnaire that measured the intensity of gadget use, types of gadget activities (academic and non-academic), and the level of concentration in learning. Data analysis was performed using multiple linear regression and Pearson Product Moment’s correlation. The results of the study showed that there was a significant negative effect between on-academic gadget use and concentration in learning. Conversely, the use of gadgets for academic purposes showed a positive correlation, although the effect was weaker than the negative effect of non-academic use. It is recommended that students manage their gadget usage time and that educational institutions provide education on effective gadget usage so that learning concentration can be improved.
KLASIFIKASI SENTIMEN PENGGUNA PADA ULASAN APLIKASI E-WALLET DI GOOGLE PLAY STORE: PENDEKATAN MACHINE LEARNING Ramadhanu, Ramadhanu; ramadhani, ramadhani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 4 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i4.94761

Abstract

Pertumbuhan aplikasi e-wallet di Indonesia mendorong pentingnya analisis sentimen untuk memahami ulasan pengguna secara otomatis. Penelitian ini bertujuan mengklasifikasikan sentimen ulasan aplikasi e-wallet di Google Play Store dengan membandingkan kinerja tiga algoritma machine learning: Extra Trees, Passive Aggressive, dan Ridge Classifier. Sebanyak 47.626 ulasan dikumpulkan melalui web scraping dan diberi label menggunakan InSet Lexicon. Data diproses melalui tahap case folding, cleaning, stopword removal, dan lemmatization. Fitur diekstraksi menggunakan TF-IDF dan diseleksi dengan Chi-Square. Optimasi hyperparameter dilakukan menggunakan Bayesian Optimization dengan pustaka Optuna, dan evaluasi dilakukan dengan Stratified 5-Fold Cross-Validation. Hasil menunjukkan bahwa Passive Aggressive Classifier memiliki kinerja terbaik dengan akurasi 95,08%, precision 92,97%, recall 89,47%, dan F1-score 90,91% pada data uji. Model ini unggul dalam hal stabilitas dan akurasi dibandingkan dua algoritma lainnya. Temuan ini menunjukkan bahwa model yang efisien dan adaptif terhadap data berdimensi tinggi sangat sesuai untuk klasifikasi sentimen teks dalam Bahasa Indonesia. Penelitian ini memberikan kontribusi dalam pengembangan sistem analisis opini pengguna untuk peningkatan kualitas layanan aplikasi digital.
Klasifikasi Otomatis Motif Tekstil Menggunakan Support Vector Machine Multi Kelas Ramadhani, Ramadhani; Arnia, Fitri; Muharar, Rusdha
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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

Abstract

Tekstur merupakan pola atau motif tertentu yang tersusun secara berulang-ulang pada citra. Tekstur mudah dikenali/dikelompokkan oleh manusia, tetapi sulit bagi mesin. Klasifikasi tekstur secara otomatis berguna dan dibutuhkan pada banyak bidang seperti industri tekstil, pendaratan pesawat otomatis, fotografi dan seni. Pada industri tekstil, klasifikasi tekstur otomatis dapat meningkatkan efisiensi proses desain motif. Motif tekstil terdiri dari banyak kelompok, sehingga diperlukan metode klasifikasi multi kelas untuk mengelompokkan motif-motif tersebut. Artikel ini memaparkan kinerja tiga metode Support Vector Machine (SVM) multi kelas: One Against One (OAO), Directed Acyclic Graph (DAG) dan One Against All (OAA) pada klasifikasi motif dari citra tekstil, dimana Wavelet Gabor digunakan sebagai pengekstraksi fitur. Kinerja SVM diukur berdasarkan parameter akurasi dan fitur Gabor diekstraksi dengan skala dan orientasi yang berbeda. Tujuan penelitian ini adalah menentukan kinerja SVM dan pengaruh jumlah skala dan orientasi Gabor yang digunakan pada klasifikasi motif tekstil. Pada simulasi digunakan 120 citra tekstil yang terbagi menjadi tiga kategori motif: bunga, kotak dan polkadot. Akurasi pengelompokan SVM mencapai kisaran 90%-100%, bahkan untuk citra yang terpotong. Pengujian dengan k-fold validation menunjukkan bahwa SVM DAG lebih baik daripada SVM OAO dan SVM OAA, dengan akurasi mencapai 78%. AbstractTexture is a repetition of a specific pattern concatenation in an image. The Texture can be defined as a repetition of pattern in an image.  The texture is easy for the human to classify, but it is not easy for a machine. Automatic texture classification is useful and required in many fields such as textile industry, automatic aircraft landing, photography and art. In the textile industry, automatic texture classification can enhance the efficiency of motif designing process. The textile motif is various and should be grouped into more than two classes; therefore a multiclass classification is required. This article discusses the performance of multiclass Support Vector Machine (SVM): One Against One (OAO), Directed Acyclic Graph (DAG) and One Against All (OAA) in classifying textile motifs, in which the Gabor Filter was used to extract the texture features. The SVM performance was measured in terms of accuracy, while the Gabor features were extracted in a different combination of scales and orientations. The purpose of the work is to measure the SVM performance and determine the effect of using various Gabor scales and orientations in textile motifs classification. We used 120 textile images with three motifs: flower, boxes and polka dot. The SVM accuracy of 90%-100% was achieved; even for cropped textile images. Using the k-fold validation, the accuracy of SVM DAG was 78%, higher than those of SVM OAO and SVM OAA
Co-Authors AA Sudharmawan, AA Abdillah, Amrullah Fajri Artha Abdul Hasan Amrullah, Taufik Abrar, Faturrahman Achmad Tavip Junaedi Adika Setia Brata Afiifah, Nailah Nur Afriani, Iim Ahda, Haryati Ahmad Said Al Zany, Nazirah Aldi Saputra Aldi, Julfi Alfarizi, Achmad Machdor Alhaddadi, Alhaddadi Almeida, Annisa Amalia, Rizqoh Amarullah, Taufik Abdul Hasan Amelia, Sarah Putri Amri Amri Andika Triansyah Andika, Putri Andleeb, Naima Andri Yanto Andriani, Rini Dewi Andromeda Andromeda Anhar Anhar Arfah, Siti Yulianty Chansah Armainingsih, Armainingsih Arthamivera, Meica Asrar Mabrur Faza, Asrar Mabrur Awalia, Nurhafipah Ayuna, Laiya Azzumar, Farchan B, Syafriadi Baharuddin Paloloang Br. Marbun, Nur Azizah Cahyadi, Maulana Abdullah Dafina Howara Dafrizal Dafrizal Dalimunthe, Irma Ramayani Damanik, Muhd. Hayyanul Damri Damri Darmina Eka Sari Rangkuti Dedy Juliandri Panjaitan Desniarti, Desniarti DEWI FITRIA, DEWI Dewi Septiani, Dewi Domo, Alfun Khoir Dwi Rahayu, Intan Dzakwan, Ibnu Efendi Efendi Eka Putri, Desi Ekardo, Apando Engkizar, Engkizar Erlin Trisyulianti Ernidawati, Ernidawati Fadhilah, Nurul Wardani Fadilla, Fadilla Fadillah, Balqis Dwi Fadillah, Jihan Husna Fadilurrahman, M. FEBRIANI, LINDA Febriyanti, Elisa Fitri Arnia Friska Prasyta Harlis Gusrio Tendra Hafo, Sungguhati Hamdani, Kemal Handari, Rahma Dewi Handayani, Revi Hanum, Yuspa Hanum, Yuspa Haowraida, Haowraida Harahap, Alwi Padly Harahap, Nurul Fazri Harry Patuan Panjaitan Haruno, Hardayani Haryati Ahda Nasution Hasibuan, Anjelina Khairani Helda Helda Hendrik Jimmyanto Heryadi, Heryadi Hidayati Rais Hizmi Wardani Hutabarat, Sri Aswita I Ketut Wiryajati Ibna, Reihana Ramadlani Ichsan Ichsan Ida Ayu Putu Sri Widnyani Ida Bagus Fery Citarsa Irawan, Ira Fauziah Irmayani, Ade Iskandar, M. Yakub Ismai, Ismai Israq Maharani Jahara, Nila Jahma, Amelia Jahrizal Jannah, Nour Javier Purba, Reynaldi Julhadi, Julhadi Junaina, Eka Kamil, Rheschy Auliya Kurniawan, Tahta Lubis, Lega Reskita Lutfitasari, Devi M. Nasir M. Rahmad M. Rahmad M. Rahmad, M. Rahmad Maqvirah, Annisa Mardianto, Putri Sekar Mastura, Fina Matondang, Rika Eliza Mayeni, Riska Meilina, Agnes Melshandika, Yola Misnasanti, Misnasanti Mualim, Mualim Muchamad Zaenuri, Muchamad Muhammad Faisal Muhammad Ichsan Muhibbah, Nur Mulyadi Mulyadi Mulyawan Safwandy Nugraha Murwaningsih, Rahayu Muzakkir Muzakkir Nada, Dina Qatrun Nafisah, Zawahir Namira, Silva Nasution, Ismail Fahmi Arrauf Nasution, Pebrijah Nazila, Laini Nazira, Rosma Nicholas Renaldo Nofriadi Nofriadi Nurdalilah, Nurdalilah Nurhafni Siregar Nurhalizah, Siti Nurhasnah Nurhasnah Oktamalasendi, Unik OKVIANI SYAFTI, OKVIANI Permatasari, Luthfia Priyandy, Deddy Okta Purba, Elda Naida Purnama, Intan Putra, Dino Adi Putra, Rio Syaenanda Putri, Annisa Fadhillah Putri, Desi Eka rahayu, Intan dwi Rahmah Rahmah Rahman, Ikhwan Rahmawati Husein, Rahmawati Rahmi, Intan Meutia Rahmi, Rhadiatul Raisa, Namira Ramadhanu, Ramadhanu Ramdani Ramdani Raudah, Siti Qira’atu Nur Refliandi , Irvan Riandi Riandi Rindiani, Rizki Rindu Twidi Bethary, Rindu Twidi Ritonga, Irmayanti Rusdha Muharar, Rusdha Rusliadi Rusliadi Safira, Nadia Safitri, Novi Angga Sahbar, Robi Said Fadhlain Sakir Sakir, Sakir Salamah, Qonita Nur Salsabila, Nada Syiva Saputra, Bobi Sari, Dwi Novita Sari, Tiara Indah Sarmila, Sarmila Sefrinal Sefrinal, Sefrinal Sembiring, Novia Rovalanda Sinaga, Dea Jufani Sipahutar, Tuti Sumyati Siregar, Irwan Efendi Siregar, Yasril Efendi Hamdy Sri Kartini, Sri Subban, Subban Suci Rahmawati, Suci Suhartanta Sukayasa Sunita, Della Surtikanti Hertien Koosbandiah, Surtikanti Suryadi, Sudi Syafitri, Linda Evi Syahrial Shaddiq Syaiful Zuhri Harahap Syamsiar, Syamsiar Tanjung, Eka Agustin Taufik Hidayat Teddy Chandra Tri Yunis Miko Wahyono Tri Yunis Miko Wahyono Tuti Alawiyah Utami, Sri Ulfa veni Veni, veni Veronica, Kristy Victor G Simanjuntak Vira Afriati, Vira Vratiwi, Septiana Wardana, Andin Mutia Wilda Susanti Windy Lestari Wiwita, Risma Yanti Herawati yanti nazmai, yanti Yasrul Sami Yaumas, Nova Erlina Zahari, Cut Latifah Zainal Asril Zainul Abidin Zuhra, Aliskha Zulfahmi Zulfahmi Zulfaneti Zulfaneti