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All Journal International Journal of Public Health Science (IJPHS) ComEngApp : Computer Engineering and Applications Journal Al-'Adalah Jurnal Psikologi Teori dan Terapan Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Jurnal Informatika Universitas Pamulang Building of Informatics, Technology and Science Piwulang : Jurnal Pendidikan Bahasa Jawa Information System Journal (INFOS) Sevanam: Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Kefarmasian Indonesia
Lestari Handayani
Pusat Humaniora, Kebijakan Kesehatan dan Pemberdayaan Masyarakat Jl Indrapura No. 17 Surabaya
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Analisis Lanjut Pemanfaatan Empiris Ramuan Seledri (Apium graveolens L) oleh Penyehat Tradisional Handayani, Lestari; Widowati, Lucie
Jurnal Kefarmasian Indonesia VOLUME 10, NOMOR 1, FEBRUARI 2020
Publisher : Pusat Penelitian dan Pengembangan Biomedis dan Teknologi Dasar Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22435/jki.v10i1.1718

Abstract

Celery (Apium graveolens L) is a very popular plant as a soup and many other vegetable menus. Celery has benefits as a medicinal plant and the efficacy has been known worlwide. Research on Medicinal Plants and Herbs (Ristoja) conducted in 2012, 2015, and 2017 has collected thousands of herbs and some of them contain celery. This study carried out further analysis on the empirical use of celery as an ingredient of traditional medicine by traditional healers (Hattra) through interviews and observations. Based on Ristoja's data, they found 90 herbs and among them there were 20 local names of herbs which are used by Hattra. The celery by Hattra was empirically identified for 10 types of diseases treatment. It was used mostly for hypertension or high blood pressure treatment and justificatied scientifically through other scientific literatures. It was recommended that celery could be use for self-medication a mild hypertension patient. Celery was safe although there are still contra indications. The celery treatment should be socialized through promotive activities in elderly posyandu activities remain under supervision of health workers
Studi Kesesuaian Sumber Daya dengan Pelayanan Kesehatan Tradisional Rumah Sakit Pemerintah di Provinsi DI Yogyakarta, Jawa Tengah dan Jawa Timur Suharmiati, Suharmiati; Handayani, Lestari; Kusumawati, Lulut; Angkasawati, Tri Juni
Jurnal Kefarmasian Indonesia VOLUME 8, NOMOR 1, FEBRUARI 2018
Publisher : Pusat Penelitian dan Pengembangan Biomedis dan Teknologi Dasar Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22435/jki.v8i1.3722

Abstract

Traditional health services (THS) in hospitals are expected to support conventional services formally applied in Indonesia. Nonetheless, heretofore there is unknown information about the suitability of the THS and existing resources. This research was conducted to analyze the suitability of resources for the means of how it is done, with the descriptive method, cross-sectional design. The sample was determined purposively by 1 (one) government hospital every regency/city that provide more than one traditional health service and operated before or since 2014. The research subject was 2 informants every hospital consisting of the unit head of traditional health services and administration staff. The results showed that most THS types were licensed, had SOPs and had appropriate resources. The existing human resources were equally good in the THS of the herb as well as the skill unit with the tools that consist of both trained and untrained physicians, diploma of traditional healers, diploma of nursing, and or high school graduates, but none of the traditional health service units had pharmacists. Types of standard herbal medicines was obtained from herbal medicine and pharmaceutical industry although in some other units also available herbs and phytopharmaca. There was a suitability of resources and treatment at the traditional health service in hospitals.
Quality of life in epilepsy: comparison between Indonesian version of QOLIE-10 and QOLIE-31 Mirawati, Diah Kurnia; Handayani, Lestari; Subandi, Subandi; Hafizhan, Muhammad; Putra, Stefanus Erdana
International Journal of Public Health Science (IJPHS) Vol 12, No 3: September 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i3.23043

Abstract

Quality of Life in Epilepsy Inventory 10 (QOLIE-10) and QOLIE-31 is used to measure patient’s quality of life. While longer version of QOLIE-31 is thought to have higher validity and reliability, QOLIE-10 is shorter and more practical to use in clinical setting. This study aimed to compare Indonesian version of QOLIE-10 and QOLIE-31. This was a cross sectional study conducted at Dr. Moewardi General Hospital, Surakarta, Indonesia. Participant were asked to complete the Indonesian version of QOLIE-10 and QOLIE-31, and data obtained then analysed to find the correlation between QOLIE-10 and QOLIE-31. A total of 51 epilepsy patients were included on this study. We observed correlation of 0.41 to 0.84 (p<0.05) for each item of QOLIE-10 with their respective QOLIE-31 subscale. We also found correlation value of 0.898 (p=.000) between total score of QOLIE-10 and QOLIE-31 showing strong positive correlation of two questionnaire. Independent T-sample test on QOLIE-10 and QOLIE-31 T-score result of p=.361, showing no statistical difference between two questionnaires. Frequency of seizure is correlated with patients’ quality of life. QOLIE-10 has strong positive correlation to QOLIE-31, which make it a useful tool to assess epilepsy patients’ quality of life.
Pemanfaatan Limbah Plastik Menjadi Barang Bernilai Guna untuk Meningkatkan Kreativitas Anak-Anak Wulandari, Oktaviana; Ardhia, Dhiana Nur; Handayani, Lestari; Diba, Berliana Farah; Nugraheni, Eva Dwi; Muhammad, Novan Aulia; Lestari, Aprili Cahya; Maharani, Yossiana Diva; Febrianto, Soka; Tsary, Keysa Inas; Aziz, Khafidh Nur
Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Vol. 9 No. 1 (2025): Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA
Publisher : Yogyakarta State University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpmmp.v9i1.70495

Abstract

Limbah plastik kini menjadi masalah paling serius yang ada di masyarakat khususnya masyarakat Dukuh Gatak. Jumlah limbah plastik selalu meningkat seiring dengan bertambahnya jumlah penduduk. Upaya pengelolaan limbah plastik agar bermanfaat bagi masyarakat dan lingkungan sekitar membutuhkan daya kreativitas yang tinggi. Salah satu upaya yang dapat dilakukan Tim KKN Universitas Negeri Yogyakarta yaitu melakukan kegiatan pemanfaatan limbah plastik dengan prinsip reuse untuk meningkatkan kreativitas anak-anak di Dukuh Gatak, Desa Beteng, Jatinom, Klaten. Metode yang digunakan adalah sosialisasi, pembuatan, penerapan, dan evaluasi. Hasil kegiatan menunjukkan bahwa pengolahan limbah plastik tidak hanya membantu mengelola sampah plastik yang sulit terurai, tetapi juga menghasilkan produk kreatif dan bernilai guna, seperti tempat sampah yang dilukis anak-anak Dukuh Gatak. Antusiasme dan partisipasi anak-anak dalam kegiatan ini mencerminkan peningkatan kreativitas mereka.
PENGELOMPOKAN DATA KONDISI MESIN SCREW PRESS MENGGUNAKAN ALGORITMA FUZZY C-MEANS Jasril, Jasril; Al Fiqri, M. Faiz; Sanjaya, Suwanto; Handayani, Lestari; Insani, Fitri
Information System Journal Vol. 8 No. 01 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i01.2133

Abstract

Kinerja mesin screw press sangat memengaruhi efisiensi dan kualitas produksi minyak kelapa sawit. Salah satu komponen penting dalam sistem ini adalah Back Pressure Vessel (BPV) yang menyalurkan uap ke berbagai stasiun proses. Penelitian ini bertujuan untuk mengelompokkan kondisi mesin berdasarkan temperatur dan tekanan menggunakan algoritma Fuzzy C-Means (FCM). Data yang dianalisis berasal dari mesin BPV PT. XYZ periode April–Mei 2024 sebanyak 23.002 entri. Tahapan penelitian meliputi seleksi data, pra-pemrosesan, normalisasi Min-Max Scaler, klasterisasi FCM, dan evaluasi menggunakan metode Elbow dan Davies-Bouldin Index (DBI). Hasil awal menunjukkan tiga klaster dengan distribusi kondisi mesin dari stabil hingga memerlukan perawatan. Metode Elbow menunjukkan jumlah klaster optimal sebanyak empat, sedangkan DBI menunjukkan dua klaster dengan nilai terbaik 0,389. Hasil ini menunjukkan bahwa FCM mampu mengelompokkan kondisi mesin secara efektif dan dapat digunakan sebagai dasar dalam pengambilan keputusan perawatan. Penelitian ini disarankan untuk dikembangkan dengan atribut tambahan.
Klasifikasi Sentimen Menggunakan Metode Multilayer Perceptron dengan Fitur TF-IDF: Sentiment Classification Using Multilayer Perceptron Algorithm with TF-IDF Features Arasy, Abdurrahman; Agustian, Surya; Handayani, Lestari; Iskandar, Iwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2052

Abstract

Media sosial, khususnya Twitter (X), telah menjadi platform utama dalam diskusi politik dan kebijakan pemerintah. Istilah dalam pengiriman pesan pada Twitter dikenal sebagai Tweet yang terdiri dari pesan dengan maksimal 280 karakter. Meskipun Tweet seringkali hanya berupateks, juga dapat menyertakan hyperlink, video, dan jenis media lainnya yang dapat digunakan untuk mengukur opini publik. penelitian ini bertujuan mengklasifikasikan sentimen masyarakat terkait pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) dengan metode Multi-Layer Perceptron (MLP) Classifier dengan pendekatan Term Frequency-Inverse Document Frequency (TF-IDF) menggunakan bahasa pemograman python. Data yang digunakan terdiri dari 300 tweet, dengan 100 tweet perkelas atau opsi untuk hasil yang optimal. Tiga kategori tersebut adalah positif, netral, dan negatif. Berdasarkan penelitian yang telah dilakukan metode terbaik mencapai F1-score sebesar 0,6767 dan akurasi 0,6667. Hasil ini menunjukkan bahwa kombinasi MLP Classifier dan TF-IDF dapat mengatasi keterbatasan dataset hingga tingkat tertentu dibandingkan metode baseline. Penelitian ini juga memberikan wawasan tentang optimasi klasifikasi sentimen dalam kondisi data terbatas, yang dapat diterapkan pada topik lain dengan permasalahan serupa
Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network Fauzan, Ahmad; Handayani, Lestari; Insani, Fitri; Jasril; Sanjaya, Suwanto
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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

Abstract

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection.
A Genealogical Analysis on the Concept and Development of Maqaṣid Syarī‘ah Nur, Iffatin; Abdul Wakhid, Ali; Handayani, Lestari
al-'adalah Vol 17 No 1 (2020): al-'Adalah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/adalah.v17i1.6211

Abstract

Maqāṣid syarī‘ah has a tremendous urgency to the process of Islamic law’s adaptability and modernization, especially in response to the increasingly complicated and crucial human problems where the treasures of classical fiqh are no longer capable to answer them. This paper studies a genealogical analysis to answer the question about the origin of the concept of maqāṣid syarī‘ah by uncovering its embryo and genes of this completely new concept. It was library research employing discourse analysis. Primary and secondary sources of works from previous and contemporary Muslim scholars discussing maqāṣid were studied and analyzed using Gadamer’s hermeneutics. The study found that from the historical perspective, maqāṣid syarī‘ah did not just emerge at the time of al-Syaṭibi, but, based on empirical data, it had been present long before his time. The literature analysis on uṣūl fiqh concluded that the figures before al-Syaṭibi had developed some works that were actually in the area of the essential and substantial values of maqāṣid syarī‘ah, however, they presented them using other words and terms so that they were considered as not of maqāṣid syarī‘ah.
APPLICATION OF K-NEAREST NEIGHBOR REGRESSION METHOD FOR RICE YIELD PREDICTION Handayani, Lestari; Alfarabi.B, Alif; Aprilia, Tasya; Wulandari, Indah; Jasril, Jasril; Ramadhani, Siti; Budianita, Elvia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.30907

Abstract

Rice plants with the Latin name Oryza Sativa are food plants that are widely used as the main food crop in various countries, one of which is Indonesia. Indonesia is ranked 4th as the largest rice consuming country in the world. This requires the availability of rice to be maintained. Unstable rice production can be a problem. One of the districts that has experienced a decline in rice production in recent years is the district of Lima puluh kota located in West Sumatra province. This requires prediction of rice production so that it can be used as a benchmark for the future. This study uses data on rice production in fifty cities from 2013 to 2023. The method used to predict is k-nearest neighbor regression (KNN Regression). The data division uses rasio 90 : 10. In testing the data used is divided into 2, namely normal data and data that has been normalized. The test results produce the smallest mean absolute percentage error (MAPE) value of 6.98% on normal data, the value of k is 6 with data division using k-fold 5. Based on the resulting MAPE value, it can be said that KNN Regression can predict rice production results very accurately.
Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine Ananda, Nuari; Fikry, Muhammad; Yusra, Yusra; Handayani, Lestari; Iskandar, Iwan
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36754

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.
Co-Authors Adha, Martin Ahmad Fauzan Al Fiqri, M. Faiz Alfarabi.B, Alif Ali Abdul Wakhid Ananda, Nuari Aprilia, Tasya Arasy, Abdurrahman Ardhia, Dhiana Nur Aziz, Khafidh Nur Baehaqi Betty Roosihermiatie DESI NURWIDAWATI Dewi Diniaty Diah Kurnia Mirawati Diba, Berliana Farah Eka Pandu Cynthia Elvia Budianita Febi Yanto Febrianto, Soka Fitri Insani Hafizhan, Muhammad Heriza, Meilinda Iffatin Nur Indah Wulandari Ismu Kusumanto Jannata, Nanda Jasril Jasril Jasril Jasril Lestari, Aprili Cahya Lucie Widowati Lulut Kusumawati Maharani, Yossiana Diva Megawati Muhammad Fikri Muhammad Fikry Muhammad, Novan Aulia Mujimin - Nugraheni, Eva Dwi Pizaini Pizaini Ramadhani, Siti Sri Anjani Stefanus Erdana Putra Subandi Subandi Sucipto Hadi Purnomo Suharmiati Suharmiati Surya Agustian Suwanto Sanjaya Tri Juni Angkasawati Tsary, Keysa Inas Wulandari, Oktaviana Yusra, Yusra