eJournal PoliTekniK TEGAL (Politeknik Harapan Bersama Tegal)
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EFEKTIVITAS WAKTU FERMENTASI ECO ENZYM TERHADAP DAYA HAMBAT BAKTERI Eschericia coli
No full textPermasalahan sampah yang selalu meningkat setiap tahun memerlukan solusi. Salah satu pemanfaatan sampah organic dengan membuatnya menjadi ecoenzym. Ecoenzym dibuat dengan mencampurkan kulit buah/sayur yang difermentasikan dengan gula. Lama/waktu fermentasi yang dibutuhkan akan berpengaruh terhadap kualitas penghambatan terhadap bakteri. Salah bakteri bakteri gram negative yaitu Eschericia coli. Tujuan penelitian ini yaitu untuk menguji efektivitas ecoenzym yang paling baik penghambatannya terhadap E. coli dengan waktu fermentasi yang berbeda. Penelitian ini merupakan penelitian eksperimen di laboratorium. Prosedur penelitian meliputi pembuatan eco enzyme dengan lama fermentasi 3, 4 dan 5 bulan, uji efektivitas antibakteri dilakukan menggunakan metode difusi sumuran. Penentuan pengaruh lama fermentasi dilihat dari luas daerah hambat yang terbentuk di sekitar lubang sumuran. Semakin luas daerah hambat maka semakin efektif eco enzym tersebut dalam menghambat bakteri Eschericia coli. Hasil penelitian menunjukan bahwa ecoenzym dengan lama fermentasi 5 bulan menunjukan hasil paling baik dengan diameter daya hambat sebesar 17,6 mm.
Penerapan Teknik SMOTE Dalam Mengatasi Imbalance Data Penyakit Diabetes Menggunakan Algoritma ANN
No full textFenomena pada pengujian data machine learning yang sering terjadi adalah data yang tidak seimbang atau sering disebut imbalanced data. Beberapa penelitian lain juga menyampaikan bahwa imbalanced data sering memberikan hasil yang tidak sesuai. Seperti kelas data seringkali mendapatkan nilai ketidakseimbangan yang disebabkan perbedaan nilai rasio antara kelas mayoritas dan minoritas. Cara mengatasi hal tersebut adalah menggunakan teknik oversampling (SMOTE), yang diterapkan pada data diabetes menggunakan algoritma ANN. Validasi keefektifan dari model yang diterapkan, dilakukan dua skema pengujian. Skema pertama, algoritma ANN tanpa oversampling SMOTE yang langsung diterapkan. Pengujian kedua, menggunakan oversampling SMOTE untuk menambah total kelas dataset agar bernilai seimbang. Pengujian yang dilakukan, menghasilkan nilai accuracy, recall, precision terbaik dengan nilai accuracy sebesar 0.92, precision 0.97, dan recall mencapai 0.94 yang menandakan, terbukti efektif dalam memberikan nilai performa pada algoritma Artificial Neural Network jika dibandingkan dengan tidak menerapkan teknik oversampling pada kasus kelas imbalance data diabetes
Apakah Pengungkapan CSR, Kekuatan CEO dan Struktur Kepemilikan Berhubungan dengan Kinerja Perusahaan?
No full textPenelitian ini bertujuan untuk mengeksplorasi dampak pengungkapan CSR, kekuatan CEO dan struktur Kepemilikan terhadap kinerja perusahaan. Pendekatan penelitian yang digunakan adalah penelitian deskriptif kuantitatif. Sampel penelitian berjumlah 88 observasi perusahaan sektor Basic Material yang terdaftar di BEI Tahun 2021-2023. Penelitian menggunakan data sekunder yang berupa laporan tahunan dan laporan keberlanjutan yang terdapat pada website masih -masing perusahaan. Analisis data panel diolah dengan perangkat lunak Eviews. Hasil penelitian menyatakan bahwa pengungkapan CSR tidak berhubungan dengan kinerja perusahan sedangkan kekuatan CEO dan struktur kepemilikan merupakan faktor yang mempengaruhi kinerja perusahaan. Struktur kepemilikan dapat meningkatkan kinerja namun sebaliknya kekuatan CEO yang terlalu besar akan menurunkan kinerja perusahaan. Adapun keterbatasan penelitian ini adalah tidak melakukan observasi terhadap lintas sektor industri sehingga untuk riset selanjutnya bisa melengkapi kekurangan ini dengan menambahkan observasi lintas sekto
THE INFLUENCE OF BANANA BLOSSOM CONSUMPTION ON INCREASING BREAST MILK PRODUCTION IN POSTPARTUM MOTHERS
No full textThis study examines the influence of banana blossom (Musa spp.) consumption on breast milk production in postpartum mothers. Banana blossoms are known to be rich in bioactive compounds such as flavonoids, saponins, and tannins, which play a role in influencing lactation hormones, particularly prolactin, and have the potential to increase breast milk production. Using a pre-test and post-test experimental design, breast milk volume was measured before and after a dietary intervention in ten postpartum mothers. The study results showed a significant increase in breast milk production following banana blossom consumption, with the average volume rising from 466.30 ml (SD = 183.30) to 2406.20 ml (SD = 373.43), p 0.001. These findings support previous studies (Buntuchai et al., 2017; Yimyam Pattamapornpong, 2022) demonstrating the effectiveness of banana blossom as a natural galactagogue and highlight its potential as an affordable dietary intervention to enhance lactation outcomes. This research provides additional insights into the role of galactagogues in postpartum care, offering a practical solution to improve breastfeeding success in mothers
THE EFFECT OF PELVIC ROCKING EXERCISE WITH A GYM BALL ON REDUCING LABOR PAIN INTENSITY IN THE FIRST STAGE AT TPMB TELLY KUSNAETI, CILEUNGSI, BOGOR
No full textNormal childbirth often induces stress and anxiety, especially in first-time mothers. To alleviate pain and facilitate fetal descent during labor, non-pharmacological methods such as pelvic rocking using a gym ball can be employed. This exercise involves moving the pelvis and waist in various directions to strengthen the abdominal, waist, and hip muscles. Using a gym ball enhances pelvic mobility and rotation while allowing exercises in sitting or upright positions, which may promote perineal relaxation and pain relief during labor. This study aimed to assess the impact of pelvic rocking exercises with a gym ball on labor pain intensity during the first stage at TPMB Tely Kusnaeti, Cileungsi, Bogor. A one-group quasi-experimental approach was utilized, involving 30 participants selected via purposive sampling. The research was conducted from August to September 2023. The hypothesis was tested using the Wilcoxon test, yielding a p-value of 0.000 (p 0.005), indicating a significant effect following the pelvic rocking exercises. Consequently, the alternative hypothesis was accepted, and the null hypothesis was rejected, confirming the significant impact of pelvic rocking with a gym ball
EXPLORING THE IMPACT OF BABY MASSAGE ON SLEEP QUALITY: A STUDY AT PRATAMA AULIA MEDIKA CLINIC, CIREBON REGENCY
No full textSleep is an important adaptation process for babies as they adjust to their environment. According to 2018 World Health Organisation (WHO) data published in the journal Pediatrics, around 40% of infants experience sleep problems. One potential intervention to address this is infant massage. This study aims to explore the characteristics of infants, assess the quality of infant sleep before and after receiving massage, and evaluate the effect of infant massage on infant sleep quality at Klinik Pratama Aulia Medika Girinata. This study used a pre-experimental design with a one-group pretest-posttest approach. The sample consisted of 30 infants aged 0-12 months who were selected using purposive sampling. Data were analysed using univariate and bivariate analyses, with Wilcoxon test used for statistical evaluation. The results showed that before receiving infant massage, most infants (n=13.52%) were categorised as having poor sleep quality. In contrast, after the massage intervention, most infants (n= 14.56%) were classified as having good sleep quality. The Wilcoxon statistical test yielded a significant p-value of 0.000, which is smaller than the alpha level of 0.05, thus supporting the acceptance of the alternative hypothesis (Ha). The findings suggest infant massage has a positive influence on improving the sleep quality of infants aged 0-12 months at Pratama Aulia
DESCRIPTION OF THE IMPACT OF ANXIETY IN ADOLESCENTS AGED 10-13 ON THE RISK OF PUBERTY DISORDERS AT SDN 4 CIKUNIR ELEMENTARY SCHOOL, TASIKMALAYA DISTRICT
No full textEarly adolescence (aged 10-12 years) is the growth phase of puberty. During this time, the body undergoes significant adaptations, mainly guided by neuroendocrine factors that are crucial for reaching sexual maturity. Pubertal symptoms can often trigger anxiety, and become a stressor that complicates adjustment to physical and hormonal changes. This study aims to analyse the impact of anxiety in early adolescents on the risk of experiencing disorders during puberty. The observational research employed a descriptive analysis approach involving 40 adolescents aged 10 to 13. The total sampling method was used, with data collection instruments including height measuring tools, weight scales, questionnaires, and checklists. The results from anxiety measurement, using the Revised Children's Manifest Anxiety Scale (RCMAS), indicated that 95% of male participants and 90% of female participants reported experiencing anxiety related to puberty. 15% of adolescent girls over nine years old and twenty-five percent of boys under nine years old experience precocious or early puberty. Among respondents, 7 (63.6%) were male, and 4 (36.4%) were female adolescents with a thin BMI. Regarding symptoms of precocious puberty, 60% were suspected to have central precocious puberty, while 40% were suspected to have peripheral precocious puberty. In conclusion, adolescents' lack of confidence and embarrassment due to taboos. There is a need for health counsellors to increase adolescents' knowledge
Klasifikasi Pengucapan Huruf Hijaiyah Berbasis Android Menggunakan CNN dengan Fitur Mel-Spectrogram
No full textMastery of Hijaiyah letters is a fundamental basis in learning the Qur'an, but data from the IIQ Community Service Institute 2021/2022 shows that 72.25% of the 3,111 Muslims tested have not been able to read the Qur'an properly. This research aims to develop an Android-based Hijaiyah letter pronunciation classification system using Convolutional Neural Network (CNN) with mel-spectrogram features. The research methodology includes collecting 8,904 voice samples from 53 participants at Pondok Tahfidz Yanbu'ul Qur'an Menawan, preprocessing data using MFCC techniques, developing CNN models, and implementing the system in the form of mobile applications with MVVM architecture. The test results showed promising performance with some classes achieving 100% accuracy and an average overall accuracy of 83.80%, although there were challenges in some classes such as “alif_dommah” and “ghaiin_dommah” which had an accuracy below 40%. The developed system successfully provides an interactive learning platform through the integration of mobile applications with the Flask API, but still requires further development, especially in expanding the dataset to overcome overfitting problems and improve the generalization ability of the model
Implementation of Radio Frequency Identification in Student Presence Applications with Multi Social Media Notification
No full textRadio-Frequency Identification or commonly called RFID is a technology that can be integrated into various softwares to increase operational efficiency and effectiveness. In an educational unit, one of the services that needs to be repaired or improved is recording student attendance. However, recording and monitoring student attendance in almost all educational units in Indonesia, and especially in Tegal City is still carried out conventionally, so that educational units cannot provide feedback to parents about their children's attendance in real time. This condition is the main basis for conducting this research. In this research, RFID technology was implemented into an desktop based application with the aim of making it easier for educational units to record student attendance automatically, and assisting schools in providing feedback about student attendance to parents through social media services (whatsapp or telegram) and increasing enthusiasm students in taking attendance. The method chosen for development is the waterfall method, this method ensures that all stages are carried out sequentially. The research application has been tested using the black box testing method, the test results indicate that the application functionality is running well
Peningkatan Keberagaman Data untuk Klasifikasi Penyakit Diabetes Berbasis Stacking Ensemble Learning
No full textDiabetes cases are becoming more common in the late years. Diabetes attacks not only parents, but also children. The development of diabetes is not far from the lifestyle and diet that we live on a daily basis. Therefore, early detection of diabetes is essential because the earlier the disease is detected, the easier it is to treat. In the process of detecting disease based on factors, the cause can be predicted with data mining. The aim of this research is to increase data diversity so that it can be processed to the maximum in data mining. In the process of data upgrading, we used the imbalance learning method SMOTE-ENN combined with the method Stacking Ensemble Learning. In the search for a powerful stacking model, seven classification algorithms were involved in the experiments carried out on this study, namely: Random Forest, Decision Tree, Gradient Boosting, Naïve Bayes, Extreme Gradiant Boost, Logistic Regression, and k-Nearest Neighbor. Four algorithms were used to be classifiers level 0 (base model), namely kNN, Gradient Boosting, decision tree, and random forest, while Random Forest was used again to be classifier level 1. (meta model). With these combinations, the accuracy obtained is 97.3%. These are the highest results when compared to individual algorithms